Accurate navigation is essential for underwater vehicles like AUVs, which often operate in deep or remote areas. However, complex ocean dynamics, cumulative inertial-measurement unit (IMU) drift, and diverse noise sources often result in erratic and unreliable position estimates. To overcome these challenges, we proposed a method that combines underwater acoustic signals with onboard motion sensor data to improve the underwater position tracking system. The proposed system uses a long baseline (LBL) acoustic array of surface buoys to capture the Time-difference-of-Arrival (TDoA) of a multi-pulse beacon. We extract arrival times using a superimposed-envelope-spectrum (SES) detector, which exploits the beacon’s periodic structure to stay reliable even in heavy noise. These acoustic measurements are fused with six-degree-of-freedom IMU data using a particle filter (PF). The filter suppresses IMU drift and reveals how long dead-reckoning remains reliable before an acoustic update becomes essential. Simulation results demonstrated that our PF-TDoA fusion method achieved up to 40% reduction in mean localization error compared to traditional fusion filters and optimization method. In the experiment, we compared the simulated IMU prediction with real-world acoustic measurements, and the resulting fused position estimated remained within 3 m of Global Positioning System (GPS)-reported trajectory, demonstrating robust performance under operational conditions. • We introduce a feature-based TDoA detector that reduces false alarms in heavy noise. • We fuse 6-DOF IMU and acoustic ranges in a PF to schedule pings only when needed. • We present a geometry-aware robust NLLS solver for accurate 3-D trilateration.

Underwater Acoustic Communication (UAC) has garnered significant attention due to its applications in marine science, defence, and exploration. With the vast majority of the Earth’s surface covered by water, there is a growing demand for reliable and effective communication techniques in underwater environments. However, the underwater acoustic channel is the most challenging channel due to its harsh characteristics, which significantly hinder the reliability and performance of UAC systems. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address high Doppler and high mobility scenarios. It has demonstrated tolerance to fast time-varying channel characteristics and enhanced resilience to Doppler shifts and multipath interference. These make OTFS suitable for the complex underwater environment. This study presents the first comprehensive review of the key challenges and state-of-the-art research and development of OTFS in underwater operations. It highlights the suitability and relevance of OTFS modulation for UAC. It also reviews recent advancements in channel estimation techniques tailored for OTFS-UAC systems. Potential future directions in sparse signal recovery for robust channel estimation and equalization, adaptive techniques, energy efficiency, and possible MAC protocols are also discussed to build a foundation for the next-generation UAC systems.

The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This paper introduces an Adaptive Guard band and Power control resource allocation scheme for mission critical applications (AGP-MCA), specifically designed to improve underwater communication. The AGP-MCA framework optimizes the acoustic spectrum based on the criticality of IoUT applications. AGP-MCA dynamically adjusts guard bands to effectively mitigate Doppler caused by mobile nodes and strategically manages transmission power to reduce power consumption significantly, and handles non-critical data through buffering. We formulate a comprehensive mathematical optimization model and employ a Whale Optimization Algorithm (WOA)-based meta heuristic approach to achieve near-optimal solutions while ensuring minimal computational complexity. Extensive simulations demonstrate that AGP-MCA enhances throughput, reduces both end-to-end delay and power consumption, and consistently outperforms existing protocols and configurations without adaptive guard bands. Further, it offers a robust and power-efficient solution for real-time mission-critical IoUT applications.

IoT sensors are made of physical materials, and due to natural decay in materials, sensor data drifts over time. Even though sensors are calibrated after deploying at the site, the accumulation of errors in sensor measurements due to sensor drifts renders the data progressively irrelevant, creating significant issues for end applications. In this article, we propose a software-driven drift detection and calibration framework based on probabilistic observation in latent space using variational autoencoders (VAEs). The proposed method utilizes the latent distribution of the generative model from sampled observational data, which are collected during the calibration phase of the deployed sensors. Variational inference in VAEs is employed to approximate the true posterior distribution for detecting sensor drifts, incorporating metrics such as Kullback-Leibler (KL) divergence. Additionally, reconstruction loss is utilized for calibrating the sensors. Both simulated and real-world sensor data are used to evaluate the proposed method. Experimental results demonstrate significant improvement over existing drift detection and calibration techniques.

Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic Networks (UWAN) devices, coupled with the challenges associated with battery replacement or recharging, intensifies the challenges. Underwater acoustic communications are heavily constrained by available resources (e.g., very limited bandwidth, and limited energy storage). Consequently, the role of medium access control (MAC) protocol which distributes available resources among nodes is critical in maintaining a reliable underwater communication system. This study presents an extensive review of current research in MAC for UWAN. This study presents an extensive review of current research in MAC for UWAN. The paper explores the unique challenges and characteristics of UWAN, which are critical for the MAC protocol design. Subsequently, a diverse range of energy-efficient MAC techniques are categorized and reviewed. Potential future research avenues in energy-efficient MAC protocols are discussed, with a particular emphasis on the challenges to enable the broader implementation of the Green Internet of Underwater Things (GIoUT).

For wearable devices with constrained computational resources, it is typically required to offload processing tasks to more capable servers. However, this practice introduces vulnerabilities to data confidentiality and integrity due to potential malicious network attacks, unreliable servers, and insecure communication channels. A robust mechanism that ensures anonymous authentication and key agreement is therefore imperative for safeguarding the authenticity of computing entities and securing data during transmission. Recently, Guoet al.proposed an anonymous authentication key agreement and group proof protocol specifically designed for wearable devices. This protocol, benefiting from the strengths of previous research, is designed to thwart a variety of cyber threats. However, inaccuracies in their protocol lead to issues with authenticity verification, ultimately preventing the establishment of secure session keys between communication entities. To address these design flaws, an improved ultra-lightweight protocol was proposed, employing cryptographic hash functions to ensure authentication and privacy during data transmission in wearable devices. Supported by rigorous security validations and analyses, the proposed protocol significantly boosts both security and efficiency, marking a substantial advancement over prior methodologies.

Encrypted sharing of Internet of Medical Things (IoMT) data is essential for facilitating collaboration, safeguarding patient privacy, and advancing clinical research. However, existing encryption schemes face numerous challenges in multi-user environments. Traditional proxy re-encryption requires exclusive ciphertext for each user, which is evidently unsuitable for IoMT's multi-user scenarios. Meanwhile, attribute-based encryption provides flexible data access control, but its complex computations and high resource demands limit its use in large-scale IoMT environments. Additionally, challenges like single-point failure and redundant backups emerge in ciphertext storage. To address these challenges, we propose a dual-cryptography mechanism integrating enhanced proxy re-encryption and attribute-based encryption. Our scheme enables unified ciphertext access for authorized users while applying attribute encryption exclusively to small data keys. To mitigate potential data loss from storage server failures, we propose a decentralized ciphertext storage and recovery mechanism with verifiable secret sharing. Furthermore, we implement decentralized ciphertext storage using verifiable secret sharing, ensuring recoverability from server failures. Formal analysis proves confidentiality under the random oracle model. Experimental results demonstrate high security strength, computational efficiency, and robustness. The solution prevents single-point failures, resists collusion attacks, and maintains traceability through blockchain-integrated audit trails.

Underwater wireless communication networks (UWCNs) can support a wide range of applications in the underwater domain including: mining and drilling, coastline monitoring, border surveillance and submarine/mine detection. Some of these applications are sensitive in nature (e.g., military) and demand stringent security requirements for data communications. In order to prevent malicious attacks (e.g., jamming) in these UWCNs, robust security countermeasures must be implemented. Additionally, sensitive data communications must be protected. However, computationally expensive security protocols, such as encryption, can severely shorten UWCN lifetime, where battery-powered nodes already suffer from scarce energy supplies. In this work, we exploit content caching as a countermeasure for jamming and utilize selective encryption of sensitive data to simultaneously maximize network security and node residual energy in UWCNs. Our work formulates the joint security and residual energy maximization challenge as an optimization problem in which results indicate that the proposed technique can guarantee secure communications without sacrificing network lifespan.

An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations.

Space-Air-Ground Integrated Network (SAGIN) has recently emerged as a viable solution for reliable transmission, high data rates, and seamless connectivity with extensive coverage. However, the characteristics of the computation and communication devices located at various levels of SAGIN make application placement within such environments a challenging task. Real-time service expectations and resource requirements of applications further intensify this issue, and push the domain to operate beyond its capacity, resulting in uneven delays and significant overhead. Taking these constraints into account, SAGIN’s application placement problem can be expressed as a multiobjective optimisation problem. This paper aims to solve such a problem using a Dynamic Weight-configurable Harris Hawks Optimisation (DW-HHO) algorithm, considering diverse application contexts such as deadlines, resource usage and the number of application activities. It simultaneously minimises application total service time and host resource overhead with a robust global search. The performance of the proposed solution is compared with benchmark metaheuristic solutions such as PSO, NSGA-II, Greedy and Random. Experimental results demonstrate that DW-HHO outperforms other benchmark metaheuristic solutions in optimising resource utilisation and service delivery time of applications in SAGIN environments. The proposed DW-HHO demonstrates notable improvements over existing methods. Specifically, when evaluating the total service time for PSO, NSGA-II, Greedy, and Random, DW-HHO outperforms these methods by 7.28%, 9.07%, 13.01%, and 14.97%, respectively.

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and cellular networks. These challenges hinder the deployment of intelligent information services in such environments. To address these challenges, we propose a distributed task processing platform (${DTPP}$) designed to provide satisfactory performance for executing computationally intensive applications in infrastructure-less environments. This platform leverages numerous distributed homogeneous nodes to process the arriving task locally or collaboratively. Based on this platform, a distributed task allocation algorithm is developed to achieve high task processing performance with limited energy and bandwidth resources. To validate our approach,${DTPP}$has been tested in an experimental environment utilising real-world experimental data to simulate IoT network services in infrastructure-less environments. Extensive experiments demonstrate that our proposed solution surpasses comparative algorithms in key performance metrics, including task processing ratio, task processing accuracy, algorithm processing time, and energy consumption.

Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network’s relentless growth allowed real-world data and created users’ traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions’ continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

In Industrial Internet of Things (IIoT), the volume of generated traffic can be high, causing concerns over issues, such as high energy consumption and longer access delay. Caching IIoT content at the network nodes is a promising solution to address these concerns. However, cached IIoT content at the network nodes is vulnerable to malicious attacks, which can cause significant disruptions in industrial operations. In order to monitor and prevent such malicious activities, trusted systems/nodes with supervisory control can be placed at the network nodes. In this article, we introduce the concept of the trusted caching node (TCN) for IIoT. TCNs cache popular IIoT content and ensure robust security to the connected links. This article finds the optimum location for the TCNs and selects secured-routes for content distribution. We formulate a joint optimization problem and minimize the cost related to probable content breach and energy consumption. We also design a low-complexity heuristic algorithm to solve this problem in real-time. The simulation results demonstrate that our proposed solution achieves significant performance gain and ensures secured IIoT content delivery.

Proactive content caching at the network-edge is envisioned as a key technique to reduce backhaul congestion and latency in popular content delivery. Data storage units, however, require additional energy, which offers a challenge to researchers who intend to reduce energy consumption up to 90% in next generation networks. The concept of powering data storage units by renewable energy aligns with this energy reduction target. In this article, we introduce a proactive caching strategy, which caches content in helper nodes in a cooperative manner considering intermittent renewable energy and variable content load for various hours of the day. The hypothesis is that the amount of renewable energy available to various helper nodes varies depending on the node location and time of the day. The content load across helper nodes also varies with time. As such, if helper nodes work cooperatively by considering the available renewable energy and content load at each helper node, the aggregate non-renewable energy consumption for content caching can be further reduced. We model the research challenge as an optimization problem. Furthermore, we also present a heuristic solution. Our proposed scheme achieves a significant reduction in non-renewable energy consumption by 23% over the existing green solution.

Streaming services have become increasingly popular in recent years, causing an upsurge in video traffic. The continuing upsurge will put enormous stress on existing network infrastructures. This has motivated researchers to find innovative solutions to address this challenge. Proactive content caching at the network edge is considered one such innovative solution, which can reduce backhaul congestion during evening peak hours. Consequently, researchers have introduced numerous approaches to cache content at the network edge. While the focus so far is on reducing the congestion, security issues in relation to content breach at caching nodes have not been adequately addressed so far. Content storage units at the network edge are more vulnerable to malicious attacks, and the loss incurred in this process can be significant. In this paper, we introduce a security aware caching technique to reduce the amount of potential loss caused by security attacks. We investigate the trade-off between the savings achieved from the implementation of proactive caching at caching nodes and the probable loss caused by security attacks. We model the research question as an optimization problem, and provide an optimal solution, which selects the suitable content to be cached at the most suitable nodes so that the loss caused by content breach becomes marginal. Simulation results show that our proposed technique significantly minimizes the loss caused by security attacks.

While the concept of ultra-dense small cell networks (SCNs) has brought a number of significant opportunities for the telecommunication industry, it has also introduced a major challenge for researchers, who must develop techniques to reduce the sharp increase in power consumption that will be required to backhaul traffic from SCNs to the core network. In this research, we investigate the green backhauling challenge for a fifth generation (5G) wireless communication network that uses both the passive optical network (PON) and millimeter wave (mmWave) backhauling to support its diverse groups of customers and applications. Our approach is based on the fact that the energy efficiency figures for the PON and the mmWave technologies are different under a given load condition. The PON technology is more energy efficient under heavy load conditions, whereas the mmWave technology offers better energy efficiency under low load conditions. As such, in response to varying traffic loads during various hours of the day, a fixed backhauling strategy is insufficient to guarantee the minimum power consumption and the required data rates. We formulate an optimization problem that considers the estimated hourly traffic load and determines the most energy efficient backhauling strategy for various hours of the day. Considering the complexity of the optimization problem, we also propose an energy efficient heuristic solution to solve this problem. Simulation results indicate that the proposed solution provides up to 32 percent more energy savings than the existing solution.

The concept of heterogeneous networks and small cells is likely to be a key addition to the fifth generation (5G) wireless communication standard, the prospect of which has motivated 5G researchers to investigate the coverage and integration of various small cells. Heterogeneous networks with small cells offer key benefits such as high frequency reuse, huge data rates and low power consumption for mobile nodes. High frequency reuse is particularly important given the exponential growth of data rates and the looming frequency scarcity problem. Another major consideration for 5G is the energy efficiency as with the exponential growth of demand, power consumption in the information and communication technology (ICT) sector is also expected to significantly increase. While the concept of small cells in heterogeneous networks addresses the frequency scarcity problem to a great extent, unless otherwise carefully managed, a large number of uncoordinated and lightly loaded small cells significantly increase the overall power consumption, contrary to the green communication target of the 5G standard. In this paper, we focus on an energy efficient resource management and planning system that investigates the impact of growing number of small cells in a macro cell. We present an analytical model for calculating the optimum number of small cells that need to be kept awake at various hours of a day for meeting the quality of service demands from all users. Simulation results show significant power consumption improvement than an existing model.

The deployment of mobile small cells has been identified as an effective strategy in delivering high data rates and providing seamless connectivity to a group of vehicular users. Although this approach can facilitate high data rates and ubiquitous wireless access, it introduces a new set of challenges, for instance, frequent changes in the interference set, requirements for more spectrum, high energy consumption and frequent handover. In this paper, we present a multi-hop mobile small cell network (SCN) that can facilitate wireless access to mobile nodes that do have direct transmission links to small cell base stations. We then formulate a solution to the frequency allocation problem for a multi-hop mobile SCN based on an optimization model. Considering the complexity of the optimization solution, we present a backhaul- aware frequency allocation solution based on the time-varying graph coloring concept. Simulation results confirm that the proposed solution outperforms an existing greedy solution by a significant margin in terms of throughput, spectral efficiency and fairness index.

ABSTRACT Call admission control (CAC) scheme serves as a useful tool for the WiMAX technology, which ensures that resources are not overcommitted and thereby, all existing connections enjoy guaranteed quality of service (QoS). CAC schemes largely rely on readily available information like currently available resources and bandwidth demand of the new call while making an acceptance or rejection decision once a new request arrives. Since wireless channels are not as reliable as wired communication, CAC scheme in WiMAX communication faces a serious challenge of making a right estimate of the usable channel capacity (i.e., effective throughput capacity) while computing the available resources in various communication scenarios. Existing CAC schemes do not consider the impact of mobility at vehicular speeds when computing the usable link capacity and available resources. The main limitation of such CAC scheme is that when a mobile node moves at a slower speed and makes a connection request to the base station (BS), the BS evaluates the situation based on the currently available information. The BS in such cases, is short‐sighted and often overestimates the available resources as it completely ignores the scenario when the SS reaches its top speed within a very short time after a CAC decision is made, causing a significant drop in usable throughput. In this paper, we address this limitation of existing WiMAX CAC schemes and propose a new CAC scheme that estimates the usable link capacity for WiMAX communication at vehicular speeds and uses this information while making a CAC decision. We also present a CAC scheme that takes the speed distribution model of a mobile node into account during the CAC decision making process. Simulation results confirm that the proposed scheme achieves lower dropping rate and improved QoS compared to existing schemes. Copyright © 2011 John Wiley & Sons, Ltd.

Poor cell-edge throughput is a well recognized problem in wireless communication technologies including 4G. Inter-cell interference (ICI), low signal to noise ratio (SNR) and inability to use an efficient modulation scheme in a low SNR environment are major problems in cell-edge regions in a multi- cell communication environment, which severely limits the spectral efficiency and cell-edge throughput. Mobility at vehicular speeds adds another dimension of problem for cell-edge users mainly because bit error rate increases exponentially with increasing vehicular speeds due to the multipath problem. 4G standards like LTE-A and WiMAX are currently considering various options to address this problem of low cell-edge throughput. One such option is to split the total radio resource among the cell center and cell-edge region, which allows frequency reuse, reduces ICI in cell-edge area, and supports CoMP transmission. In this paper, we show that simple resource splitting strategy like this, is not sufficient to support wireless communications at vehicular speeds. We propose a game theoretic model to resource management in 4G networks, which works well with nodes moving at high vehicular speeds. The ultimate benefit of the proposed scheme includes lower connection dropping rates for mobile nodes and higher revenue return for the service providers.

High speed wireless communication technologies such as Worldwide Interoperability for Microwave Access (WiMAX) have revolutionized the way of our day-to-day communication and opened opportunities for many innovative applications. The 802.6m version of WiMAX offers data rates up to 1 Gbps for fixed communications and supports mobility up to 350 km/h. While WiMAX technology's capacity to deliver high data rates in a fixed environment is beyond any doubt, the standard is not fully optimized yet for mobile communication at high vehicular speeds. At high vehicular speeds, rapid changes in surrounding environments, cause severe fading at the receiver, resulting a drastic fall in throughput and unless any proactive measure is taken to combat this problem, throughput becomes insufficient to support many applications, particularly those with multimedia contents. Bit Error Rate (BER) estimation is an integral part of any proactive measure and recent studies suggest that Nakagami-m model performs better for modeling channel fading in wireless communications at high vehicular speeds. No work has been reported in literature that estimates BER at high vehicular speeds in WiMAX communication using Nakagami-m model. In this paper, we develop and present an analytical model to estimate BER in WiMAX at vehicular speeds using Nakagami-m fading model. The proposed model is adaptive and can be used with resource management schemes designed for fixed, nomadic, and mobile WiMAX communications.

Intelligent transportation system (ITS) has enjoyed a tremendous growth in the last decade and the advancement in communication technologies has played a major role behind the success of ITS. Due to the nature of communication in vehicular environments, wireless access is considered as an integral part of any ITS system. The IEEE 1609.4 has evolved as a standard for wireless access for vehicular environment (WAVE), which describes multichannel access operations over the 5.9GHz dedicated short range communications (DSRC) spectrum. Communication Channels in the 1609.4 are grouped into service and control channels. Control channels (CCHs) are used for transmission of safety and management messages (e.g., traffic congestion data, data for emergency services) whereas service channels (SCHs) are used for the nodes to transmit service data (e.g., voice,video). In existing channel access mechanism, a node stays in the control channel to receive safety and management messages and then switches to service channel for data transmission. The key problem with this channel access mechanism is that half of the service channel intervals remain idle and unused as all nodes have to listen to control channel during that interval, which makes the WAVE system underutilized and inefficient. In this paper, we propose a medium access control (MAC) protocol for WAVE system to improve the channel utilization and reliability of safety messages. The proposed protocol has been developed based on the concept of cognitive radio and it outperforms the existing channel access mechanism by a significant margin in terms of channel utilization. Simulation results confirm that the proposed cognitive MAC protocol increases the channel utilization up to 70% compared to the IEEE 1609.4 standard, and maintains reliability for the safety related data transmission.

Voice and video over IP (VVoIP) applications are becoming very popular in Wi-Fi enabled portable/handheld devices, because of recent technological advancements and lower service costs. Video IP telephony service (an enhanced service of voice over IP (VoIP)) and video conferencing over IP networks are good examples of modern IP applications. Different from normal voice & video streaming, these applications demand symmetric throughputs for upstream and downstream. Existing Wi-Fi standards (e.g. IEEE 802.11e EDCA) however fail to provide symmetric throughput due to traffic bottleneck at access points. In this paper, we introduce a new real-time data performance attribute called IP traffic symmetry ratio (IPSR) and use IPSR to propose a round-robin scheduler with minimum overheads for video IP telephony and other similar applications. We refer to this scheduler as TI-MAC (Traffic Intelligent - MAC). The scheduler has been implemented and tested in actual hardware, and compared against IEEE 802.11e EDCA. The proposed scheduler shows significant improvements over existing standards by supporting more number of stations with strict QoS criteria.

Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by the WiFi technology. While WiFi enjoys a high penetration in the market, its hotspots are connected to the internet through wired connections, making its deployment cost very high. WiMAX has emerged as an existing new wireless technology, which provides larger coverage and higher bandwidth. Deployment of WiMAX only infrastructure, however, is highly expensive, which has motivated researchers to search for a low cost integrated WiMAX/WiFi architecture (using WiMAX as the backhaul and WiFi as the last mile technology) that supports 4G applications and provides high speed broadband services. WiMAX technology is equipped with mechanisms capable of delivering guaranteed quality of service (QoS). WiFi, on the other hand, has very limited capacity for providing QoS to the end applications. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. In this paper, we depict a converged architecture of WiMAX and WiFi, and then propose an adaptive resource distribution model for the access points. The new model is designed as an optimization problem that maximizes the QoS utility of the network. A new QoS utility function is proposed that takes the connection priority and continuity into account. Our simulation results show that our proposed scheme maintains QoS in different scenarios whereas existing other resource sharing schemes experience violation of QoS (minimum rate requirement) in 66% cases.

The IEEE 802.16e standard, also known as mobile WiMAX, has emerged as an exciting mobile wireless communication technology that promises to offer both high throughput and guaranteed quality of service (QoS). Call admission control (CAC) scheme serves as a useful tool for WiMAX, which ensures that resources are not overcommitted and thereby, all existing connections enjoy guaranteed QoS. Existing CAC schemes largely depend on readily available information like currently available resources and bandwidth demand of the new call while making an acceptance or rejection decision once a new request arrives. Since wireless channels are not as reliable as wired communication, CAC scheme in WiMAX communication faces a serious challenge of making a right estimate of the usable channel capacity (i.e., effective throughput capacity) while computing the available resources in various communication scenarios. Existing CAC schemes do not consider the impact of mobility at vehicular speeds when computing the usable link capacity and available resources. In this paper, we propose a new CAC scheme that estimates the usable link capacity for WiMAX communication at various vehicular speeds and uses this information while making a CAC decision. The proposed CAC scheme takes the speed distribution model of a mobile node into account during the CAC decision making process. Simulation results confirm that the proposed scheme achieves lower dropping rate and improved QoS compared to existing schemes.

The IEEE 802.16e standard has emerged as an exciting mobile wireless broadband technology that promises to deliver both high throughput and guaranteed quality of service (QoS). Call admission control (CAC) scheme serves as a great utility for WiMAX, which ensures that resources are not over committed and thereby, all existing connections enjoy guaranteed quality of service. Existing CAC schemes largely depend on computation of available resources while making an acceptance or rejection decision once a new request arrives. Since wireless channels are not as reliable as wired communication, CAC scheme in WiMAX communication faces a serious challenge of making a right estimate of the usable channel capacity while computing the available resources at various communication scenarios. Existing CAC schemes do not consider the impact of mobility at vehicular speeds when computing the usable link capacity and available resources. In this paper, we propose a new CAC scheme that estimates the usable link capacity for WiMAX communication at various vehicular speeds and uses this information while making a CAC decision. Extensive simulation results show that the proposed scheme achieves lower dropping rate and improved QoS compared to existing schemes.

Video surveillance on public transport is a useful tool to fight against anti-social behaviour like vandalism, harassment, graffiti and terrorism. Real-time video surveillance on moving public transport faces serious technological challenges mainly due to limited throughput offered by existing communication technologies at high vehicular speeds. Success of real-time video surveillance on public transport heavily depends on future communication technologies like WiMAX. WiMAX has emerged as an exciting technology with promises to offer high throughput and improved quality of services (QoS), key requirements for video surveillance on public transport. WiMAX however, offers limited throughput at high vehicular speeds mainly because of multipath fading that causes high bit error rate at the receiver at vehicular speeds. In our previous works, we showed that it is possible to estimate the bit error rate at the receiver end at various vehicular speeds in WiMAX and accordingly, some proactive measures can be adopted to improve the throughput to some extents. Overall throughput however, may still be insufficient to support the streaming video data from all the cameras mounted on a public transport at high vehicular speeds. In this paper, we propose a new scheme that estimates utility for different cameras and puts some low utility cameras offline and thereby maintains high utility of the video surveillance system when the throughput at high vehicular speeds become insufficient. Simulation results confirm the effectiveness of the proposed scheme.

This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between IR and book-ahead (BA) call connections. BA reservation, which confirms the availability of resources in advance, is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns for the implementation of BA reservation is the need for preemption of on-going IR calls to accommodate BA calls when resource scarcity arises. Preemption disrupts service continuity of on-going calls which is considered as severely detrimental from users' perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No works have yet focused on the preemption problem of on-going IR calls at routing stage in the presence of BA calls. We present a mathematical formulation to compute the preemption probability of an incoming IR call at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of the calculated preemption probability of the incoming IR call and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms widely recommended shortest path and widest path routing algorithms in terms of IR call preemption and blocking rate. The proposed approach also yields higher network utilization and IR effective throughput.

Preemption is an efficient technique to provide available and reliable services to high priority connections in a QoS-enabled network. Preemption technique is governed by a preemption policy which makes the decision about which connections to preempt when resource scarcity is experienced. Previously proposed policies considered preemption rate, priority of connection and preempted bandwidth as the deciding criteria for preempting connections. In this paper, another new criterion, namely, user satisfaction is introduced in formulating the objective function that defines the preemption policy as an optimization problem. Simulation results show that the proposed policy incorporating user satisfaction outperforms the existing preemption policy in terms of customer satisfaction with a significant margin and performs comparably in terms of preemption rate and priority of preempted calls. The improved user satisfaction indicates higher prospects of revenue return and make the proposed policy highly attractive to the network providers.

This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between instantaneous request (IR) and book-ahead (BA) call connections. Book-ahead reservation which confirms the availability of resources in advance is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns in the implementation of BA reservation is the preemption of on-going instantaneous requests (IR) call connections. Preemption disrupts service continuity which is seen as detrimental from users' perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No work known to these authors has yet focused on the preemption problem of on-going IR call connections at routing stage. We present a mathematical formulation to compute the preemption probability of an IR call connection at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of calculated preemption probability of incoming IR call connection and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms both shortest path and widest path routing algorithms in terms of preemption and call blocking rate.

Preemption of low priority call connections to accommodate high priority call connections is a widely recommended technique in a multimedia communication network. A preemption policy is required to make decision about which connections to preempt when resource scarcity is experienced. In literature, priority of connection, preempted bandwidth and the number of preempted connections are proposed as the three basic criteria governing a preemption policy. So far revenue prospect of a communication provider, specially in relation to consumer satisfaction, has not been incorporated in preemption policy. In this paper we introduce revenue index, a metric that indicates the level of estimated consumer satisfaction, as an additional criterion to be used in conjunction with the above three. Revenue index is an indicative of long term revenue prospect of an enterprise. We present formulation of the preemption policy as an optimization problem. A heuristic to approximate the optimal solution is also derived. Simulation results show that the proposed model of preemption policy when adopted by a network provider ensures better consumer satisfaction for the end users which leads to higher revenue index for the network provider

Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and /spl epsiv/-insensitive loss function. We investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented.