Wildfire incidents have surged in frequency and severity in recent years highlighting the need for advanced technologies to predict wildfire behavior early and mitigate its impact. Recent strides in machine learning research, the increased availability of wildfire data, and computational resources have fueled the rise of data-driven approaches in wildfire management. This study aims to advance data-driven methods for predicting wildfire behavior and aid in timely decision-making and resource allocation efforts by adopting a Graph Neural Network (GNN)-based framework for predicting the burned area resulting from a wildfire ignition. GNNs have shown success in handling irregular-sized inputs and capturing the long-range dependencies inherent in geospatial data, such as wildfires, making them a viable alternative to CNNs which impose limitations on geospatial data due to their reliance on fixed-size inputs and local receptive fields. A framework is developed to represent spatial wildfire data and its influencing factors as graphs followed by the development of three distinct GNN models based on different message-passing mechanisms to process the graph-structured data. GNN models outperform CNN-based segmentation models in wildfire prediction, achieving higher AUPRC (0.4787), precision (0.4536), and AUROC (0.9377), and illustrating the efficacy of GNNs in modeling wildfire behavior by effectively capturing spatial dependencies.

In response to the growing incidence and severity of wildfires, this paper presents Vi-Net, a novel hybrid deep learning framework for next-day wildfire spread prediction. By integrating U-Net’s fine-grained spatial segmentation with the global contextual modeling of Vision Transformers (ViT), Vi-Net formulates wildfire spread prediction as a semantic segmentation task. The model is trained on a decade-long (2012–2020) multimodal wildfire dataset that integrates meteorological, topographical, and vegetation features. To address the severe class imbalance inherent in wildfire data, Vi-Net employs a Focal Tversky loss function. Experimental results show that Vi-Net achieves an F1-score of ∼97% and an Intersection over Union (IoU) of ∼94% on test data, significantly outperforming standalone U-Net and ViT models. These findings underscore Vi-Net’s potential to improve wildfire mitigation planning, resource allocation, and emergency response.

Forest fires or wildfires are becoming more prevalent across Canada. They are both beneficial and harmful. They promote forest health and aid ecological processes. However, they play a devastating role in impacting the economy of a nation and also impact the health of humans. Hence, it is important to consider all data sources relevant to forest fires or wildfires. The Canadian Wildland Fire Information System (CWFIS) calculates the danger of forest fires. The Canadian Forest Fire Weather Index (FWI) System is a critical part of CWFIS, which does not consider land vegetation in its calculations. Considering it is the vegetation that burns in a forest fire, it is important to have an insight into what types of vegetation are more prone to fires. Earth observation data for vegetation over land is now available across North America. This research primarily provides an analytical data visualization of the vegetation land cover impacted by and impacting forest fires. We look into open-source vegetation land cover data and provide insights into forest fires or wildfires. A look into the change of vegetation is also provided.

Intelligent Transportation System (ITS) applications rely on reliable data exchange, supported by the WAVE protocol through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In single-radio configurations, nodes alternate between a control channel (CCH) and service channels (SCHs), leading to a burst of queued transmissions at the start of each SCH interval. This results in the start-of-interval contention (SIC) issue, characterized by high collision rates and reduced delivery performance. Therefore, we propose a centralized contention window (CW) adaptation mechanism based on the Quantile Regression Deep Q-Network (QR-DQN), where a Roadside Unit (RSU)-hosted agent adjusts CW using only PHY-layer observations. We further combine this deep reinforcement learning (DRL)-based method with the Skip-CCH mechanism and evaluate both individual and combined solutions through simulation. Results show notable improvements in packet delivery and channel efficiency compared to legacy WAVE and Skip-CCH.

Most of the future intelligent transportation services will rely on onboard sensing and communication protocols used in modern vehicles for providing uninterrupted services such as lane change, on demand audio-video entertainment, and emergency services to end users. Most of these services generate a huge amount of big data used for analytics to take intelligent decisions. However, keeping in view of the complex decision making and limited resources, the deployment and use of these services has various challenges and constraints including data safety, intelligent decision making, and route planning. Specifically, handling emergency situations for the end users traveling on road can be considered as an interesting problem which requires an efficient solution resilient to the aforementioned constraints and challenges. Motivated from the above, in this paper, we propose a prioritize route selection strategy using Federated learning (FL). The proposed scheme first envisions a futuristic road network scenario in which vehicles rely on an onboard intelligent route movement algorithm for reaching to its destination. By assigning higher priority to vehicles on emergency duties, the proposed scheme provides an uninterrupted route discovery by facilitating them to reach their destination on time. The proposed scheme has been validated using simulations on benchmark data sets traces using various performance evaluation metrics in comparison to the other existing state-of-the-art proposals. Results obtained prove the efficacy of the proposed solution on comparison with other existing schemes in literature.

In recent years, advancements in vehicular communication has improved road safety along with passenger convenience for many applications. However, to take intelligent and timely decisions, a large number of complex operations need to be get executed on large amount of data base repositories which in turn generates a huge burden on the underlying network infrastructure leading to a large amount of energy consumption. Most of the existing solutions reported for the aforementioned problems are based upon the traditional monolithic solutions which may not be applicable in modern scenarios in this environment. Hence, to mitigate the aforementioned challenges and constraints, in this article, we propose BOOST, a connected dominating set (CDS)-aware energy-efficient clustering scheme for Software Defined Network by integrating V2I and V2V communications for reliable and seamless data delivery. The proposed scheme has been specifically designed for urban scenario to achieve effective data delivery with minimum energy consumption. By leveraging the benefits of CDS on roadside communication infrastructure, BOOST is able to adapt with varying traffic conditions to provide seamless scalability with minimum energy and network overheads. The proposed scheme has been evaluated using various performance evaluation metrics in comparison to the existing benchmark schemes. The results obtained demonstrate its superior performance by 3% to 4% in terms of energy-efficiency, network overhead, packet delivery rate, and network throughput in comparison to the existing benchmark schemes.

Prevention of forest fires increasingly impacted by climate change is essential to maintain ecological balance, preserve natural resources, prevent economic loss, and protect human and animal life. Data for forest fires is available from multiple sources and is huge. Federated learning can be implemented to distribute the computing across multiple edge devices by saving transmission costs, protecting data privacy, and maintaining security with no single point of failure as local models exist across multiple resources in different geographic regions. The proposed framework extends the Spatio-Temporal Agnostic Subsampling (STAS) technique by distributing the data into multiple computation nodes to leverage federated learning. It was found that the models trained using federated learning on weather data gained on average 0.3 in F1 for classifying the occurrence of fire. This study also demonstrates how to optimally choose the sources of data for either predicting the occurrence of fire or the severity of fire.

Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning’s role in addressing forest fire challenges.

This research provides a spatio-temporal agnostic framework based on subsampling to generate generic deep learning models using publicly available weather data and to predict the probability of forest fire and severity. The aim is to show that this framework can be used to subsample and generate a balanced dataset for generic deep learning models to improve predictions for forest fires. The framework works for binary classification and regression deep learning models. It also works with limited variations between fire and non-fire data. Using this framework, 45 of the binary classification models built produced an F1Score greater than 0.95 while 35 of 54 regression models produced an R2Score greater than 0.91.

Cyber-security experts often require the help of an automated process that filters and classifies network attacks. To apply specific preventive measures for securing networks, the classification of the attack type is the key. Many Machine Learning (ML) models have been proposed as a base for Network Intrusion Detection (NID) systems. However, their performance varies based on multiple factors. For instance, an ML model fitted on a highly imbalanced dataset can be biased toward over-represented attack types. On the other hand, paying attention only to the ML model’s performance in the minority classes can negatively affect its performance in the majority classes. This paper proposes an NID system that addresses the issue of imbalanced datasets and uses Convolutional Neural Networks (CNN) to classify the different attack types. We compare the performance of our proposed system to other systems that use: Random Over-Sampling (ROS), Synthetic Minority Oversampling TEchnique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Generative Adversarial Networks (GAN). Using the NSL-KDD and the BoT-IoT datasets for benchmarking, we show that our proposed system performs well in the minority classes: recall scores of 70.50% and 72.08% on the User to Root (U2R) and Remote to Local (R2L) attack classes of the NSL-KDD dataset, respectively, while maintaining an overall False Alarm Rate (FAR) of 6.50% and a recall of 90.46% on the binary classification task. Our proposed system scores a weighted average F1-Score of 99.45% on the multi-class classification task using the BoT-IoT dataset.

Self-Sovereign Identity (SSI) empowers users to govern their digital identity and personal data. This approach has changed the identity paradigm where users become the central governor of their identity; hence the rapid growth of the SSI model. Utilizing the security and privacy properties of blockchain, together with other security technologies, SSI purports to provide a robust security and privacy service. However, this governing power for users comes with a greater accountability and security risk, as not all users are capable or trained in its use and therefore in its efficient application. This trade-off requires a systematic evaluation of potential attacks on the SSI system and their security risks. Hitherto, there have been no noteworthy research studies performed to evaluate potential attacks on the SSI system and their security risks. This paper proposes an easy, efficient and economical approach to perform an evaluation of potential attacks on the SSI system and their security risks. This approach utilises a combination of an attack tree model and risk matrix model to perform this evaluation of potential attacks and their security risks, in addition to outlining a systematic approach including describing the system architecture and determining its assets in order to perform this evaluation of potential attacks and their security risks. This evaluation work has identified three potential attacks on the SSI system: faking identity, identity theft and distributed denial of service attacks, and performed their security risk evaluation utilising the proposed approach. Finally, this paper has proposed several mitigation strategies for the three evaluated attacks on the SSI system. This proposed evaluation approach is a systematic and generalised approach for evaluating attacks and their security risks, and can be applied to any other IT system.

YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.

A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) AutoEncoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE’s reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs’ reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to ~ 98% in some scenarios.

The ever-growing demand for electricity in the residential sector results in creating a severe burden on electric grids. However, with the emergence of smart homes (SHs) and smart grids (SGs), this burden can be reduced to some extent. To address this issue, we propose an energy management system in this paper which manages the power requirements of SHs automatically according to the utility constraints and user priorities. The proposed system is based on a heuristic technique, which considers the user's priority and power available from the grid as well as distributed energy resources for scheduling of appliances. It works by dividing the appliance scheduling problem in an SH into subproblems for different time slots. Then, a heuristic solution is designed for each subproblem. The instantaneous load demands are handled in real time to comply with the available power from the grid/utility. The data from different SHs is gathered to test the performance of the proposed scheme in real time. Results show that the proposed scheme efficiently manages the load demand of the SH with respect to power available from the utility, battery energy storage system, and user preferences.

The problem of extracting insights from signals is a very interesting and challenging task. This problem finds its way into the task of detecting malware in wireless devices by considering their power consumption signals. Relying on the fact that every single action on-board (whether hardware or software driven actions) will be reflected as a change in the device's power consumption; consequently, leaving a trace (by malware) in the power consumed by the device is something inevitable. Motivated by the powerful capabilities of deep learning in extracting features unsupervisedly, this paper proposes deep learning based detection methodology. The methodology makes use of time-frequency representation (TFR) of signals to resemble informative visual textures. The assumption is that TFRs (2-D images) construct textures that capture valuable information out of 1-D signals. Following that, Histograms of Oriented Gradients (HOG) of TFR images are computed. The HOG information is treated as images that contain better discriminative features. Finally, a convolutional neural network (CNN) model is trained to accurately classify these signals and detect the anomalous behavior. We have validated the effectiveness of the proposed methodology on a cybersecurity application in the domain of wireless devices. The experimental results confirm that proposed methods can be used to detect the presence of malwares in smartphones with high accuracy, and can also outperform previously reported methods with ~9% to 17% detection performance gain.

There are different types of messages containing different priorities in vehicular ad hoc networks. Hence, queue rescheduling is required according to priorities of arrived messages. In this study, a data structure with less computational complexity is proposed to minimise queuing delay. Further, to maintain quality of service, preemptive priority is applied to time‐bound safety messages by transferring non‐safety messages to other bands using the concept of cognitive radio technology. The time‐bound messages are transmitted using the dedicated short‐range communication spectrum without the need for spectrum sensing by vehicles. The other messages with no deadline constraint are switched to other bands near‐dedicated short‐range communication spectrum. The results show that 6.25% improvement in packet delivery ratio of cognitive radio‐enabled preemptive priority is achieved in comparison to existing cognitive radio protocol. The delay shows a slight increment of 1.1%. The packet delivery ratio of cognitive radio‐enabled non‐preemptive priority is improved by 3.24% while the delay is improved by 3.17%. The data storage required for storing sensing data of 50 channels for 10 days is only 45 Mb.

Summary We propose a model‐based test generation methodology to evaluate the impact of the interaction of the wireless network and application configurations on the performance of mobile networked applications. We consider waiting time delay to model wireless network quality. We classify mobile applications into two groups. Group I represents applications where end‐user experience is mainly affected by waiting time delay during service consumption, while group II represents applications where end‐user experience is affected by waiting time delay before service consumption. Test generation is formulated as an inversion problem. However, for group I applications, solving the inversion problem is expensive. Therefore, we utilize metamorphic testing to mitigate the cost of test oracles. We formulate metamorphic test generation as maximization of the distance between seed and follow‐up test cases. Two test coverage criteria are proposed: user experience and user‐experience‐and‐input interaction. Network models are developed for a mobile device that has network access through a WiFi hot spot and uses either transmission control protocol or user datagram protocol. Two mobile applications are used to demonstrate the methodology: multimedia streaming and web browsing. Application of the methodology when user actions are taken into consideration is also addressed. The effectiveness of the methodology is evaluated using two metrics: the incurred time cost and redundancy in the generated test suite. The obtained results show the advantage of casting test generation as an inversion problem, compared with random testing. For apps with intensive performance models, combining metamorphic testing with the methodology has tremendously reduced the cost of test oracles.

The problem of detecting malware in mobile devices is becoming increasingly important. While most of the mobile devices run on very limited resources, having anti-viruses installed on-board is not very practical, especially in IoT devices. Even if such tools exist, malware could hide or manipulate their fingerprint, making them not easy to detect. Thus, having effective countermeasures for after malware intrusion is paramount. In this work, we utilize deep learning ability to learn multiple levels of representations from raw data to classify power consumption signals obtained from smartphones. The objective is to build a framework that can intelligently tell if the smartphone has a malware or not by only monitoring its power consumption. Validation tests confirm that the proposed framework show that information contained in the measured power consumption of smartphones can in principle be used to identify malware existence and further can tell how active malware is with very high accuracy.

Electric vehicles (EVs) are considered as a flexible load in smart grids. This flexibility promotes the EVs to be good candidates for providing the grid with ancillary services, such as regulation services. A group of EVs controlled by an aggregator can collectively work as a regulation reserve in the electric grid. However, a fast response to the regulation commands is crucial to providing reliable regulation service. Typically, the response time to the regulation command requires to be less than four seconds. In this paper, we precisely evaluate the expected time delay from the instant when an aggregator server sends a regulation command to n EVs to the instant when all the EVs' responses are received successfully by the server. To achieve this goal, first, a realistic communication structure between the aggregator server and the EVs is considered. Second, the wireless link between the access point (AP) and the EVs is accurately modeled in order to estimate the average delay. The model is based on Markov chain representation for the wireless IEEE 802.11 MAC protocol. Two important factors are considered in this model. First, the packet loss probability due to the lossy wireless environment has been incorporated into the model. Second, the transition stages for the contention window size of 802.11 MAC protocol to reach the saturation stage is taken into account. The model has been validated by means of extensive simulation using the well-known Network Simulator 2 (NS2) tool. Our analysis shows that one AP is capable of handling up to 1000 EVs without violating the 4-s latency limit when the probability of packet loss is 0.01. However, this number decreases significantly, less than 500 EVs, when the wireless link is experiencing a significant packet loss probability of 0.2. Further, we show that by commanding a subgroup of the charging EVs, it is possible to achieve the same regulation service but with a lesser response time, compared to commanding all charging EVs every time.

Different applications in smartphones result in different power consumption patterns. The fact that every application has been coded to perform different tasks leads to the claim that every action onboard (whether software or hardware) will consequently have a trace in the power consumption of the smartphone. Even though the power consumed by the application might not be the same every time it is used, there still remains a similarity in the power consumption pattern. An anomalous behavior on the smartphone would result in a reduction in the similarity of the power consumption pattern. This change in similarity can be used to detect the presence of anomalous behavior of smartphones. We have proposed two approaches to detecting anomalous behavior on smartphones based on the power consumption pattern. The first approach is based on signal processing and the second approach explores the area of statistical learning in detecting malware. The two approaches have been analysed, evaluated, and compared. It has been observed that the signal processing method of detection performed better for anomalous behavior of lower intensity and the statistical learning method performed better for higher intensity anomalous behavior. It was also observed that both the methods are complementary.

Cooperative intelligent transport system (C-ITS) is an emerging paradigm. It aims at wirelessly increasing the awareness of vehicles of their neighborhood traffic via cooperative awareness messages for safety and non-safety purposes. Toward an efficient C-ITS realization, this paper presents a simple, yet effective, broadcast scheme for I2V/V2I communications. In this scheme, road information or safety related messages are reported from the C-ITS to vehicles via road side units (RSUs) using acknowledged radio broadcasts. Vehicles report any newly observed road status and/or safety related information piggybacked in their acknowledgments. The RSUs report these messages to the rest of the vehicles under its coverage via repeated broadcasts using adaptive inter-broadcast interval based on vehicle density and estimated average channel quality. The scheme aims at decreasing unnecessary vehicle-to-vehicle (V2V) broadcasts, which leads to potentially decreasing the risk of broadcast storms. The paper introduces a comprehensive analysis of the delay performance of the proposed scheme considering practical physical channel conditions. The analysis, supported by validating simulation results, indicates the applicability of the proposed scheme as it can achieve low information transfer latency for high density roads.

The paper studies the scenario of wireless multicast with a single transmitter and a relay that jointly deliver successively refined (or multi-resolution) sources to multiple receivers. By taking the end-to-end mean square error distortion (EED) as the performance metric, the problems of power allocation at the transmitter and relay are formulated. Due to nonlinearity of the formulations, a generalized programming algorithm is developed to obtain near optimal solutions. Case studies are conducted to verify the proposed formulations and solution approaches. The results show the advantages of using a relay assisted multiresolution approach.

Mobile devices are constrained by the limited capacities of their small batteries. However, profiling the energy consumed in the task execution is crucial to help the developers to build energy efficient applications. Therefore, the major challenge in the profiling approach is to accurately estimating the energy consumed for an application by the hardware components, such as CPU, memory, storage unit, and network interfaces. In this work, we develop and validate hardware and software profiling models and procedures. We profile smartphone CPU, where we consider multi-core CPUs and the impact of Dynamic Voltage and Frequency Scaling mechanism on the power consumption. In addition, we profile smartphone storage unit by taking into account the writing and reading rate to the unit. Moreover, we experimentally validated these profiles on two diverse smartphones with different versions of operating systems. The experimental results reveal that our profiles are able to estimate the application energy accurately.

Delay Tolerant Networks (DTN) comprise nodes with small and limited resources, such as power and storage space. The constraint of resources, together with the mobility and sparsity of DTN nodes, trigger an intermittent connection among the nodes and require delay tolerance of their applications. In such a challenging environment, decentralized routing protocols are implemented with the main concern of maximizing data delivery and minimizing resource usage. These protocols rely on the participation of the network nodes in receiving and storing data packets, collecting and processing information about the network topology to find the next best-hop, and replicating and spreading packets of each other. From a network perspective, all nodes are required to participate in delivering packets of each other. From a node perspective, minimizing resource consumption is the most important. We define fair cooperation as the degree of cooperation where all nodes are satisfied with their participation in the network routing services. We propose a distributed game theoretic approach that computes a node utility function to achieve fair cooperation. We implemented the method into several DTN routing protocols and compared their performance. Results show that by tuning the parameters of the utility function, fairness among the nodes is improved, and delivery cost is reduced, which motivates the nodes to cooperate.

Integration of renewable energy sources and Electric Vehicles (EVs) into smart grids comes with significant challenges. The uncertainty of the short-term forecasted energy from renewable sources increases the variability of the net-load in the grid. Also, EVs' charging could exacerbate the load peak in the grid if charging is not coordinated. In this work, firstly, we study the impact of the variability of renewable sources on the short-term forecast of the net-load in the electric grid, and a model of the net-load forecast error is developed. Secondly, a novel online charging algorithm for EVs is proposed not only to shift EVs' load from the system peak period to more desirable period, but also to decrease the variability of the net-load in the grid. Simulation results show that our algorithm outperforms the traditional scheduling algorithms which optimize the overall load in the system based on short-term load forecast.

Although most smartphones today have both cellular data and WiFi capacity, network-selection techniques typically only allow for a single data interface to be used at a time. This leads to several problems including the interruption of existing connections when switching interfaces, complete loss of connectivity when the selected network is not functioning correctly, and significantly less bandwidth to the device than is possible if the available wireless capacity is aggregated. While interface bonding is not new, aggregating two such diverse networks is particularly challenging given that different wireless technologies, and even different networks of the same type, offer inconsistent link parameters, presenting dynamically fluctuating bandwidth, latency, and packet loss. In this paper we present a simple, but extremely efficient, hashing technique for multi-interface packet scheduling. We have implemented our system with aggregation proxies running in well-connected data centers and client code running on a Galaxy Nexus running Android 4.1. Our experimental results show that our prototype never loses connectivity even when networks fail as long as at least one network is functioning, does not break connections as interface selection is adjusted, and can increase bandwidth to the smartphone to the available aggregated network capacities. Transmission time is reduced by up to 60% vs. using a single network interface.

In this paper, we propose a practical decision engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the decision engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the decision engine. The practicality of the decision engine is demonstrated by means of real-world computing scenarios.

C LOUD computing is the future but it will not be if users' security concerns remain unaddressed.Cloud security issues include data privacy, data integrity, and service availability, among others.Due to the extra computing involved, security controls often incur a certain amount of performance degradation in cloud computing where performance is crucial and its computation and communication complexities are already high.This poses challenges to system developers with regards to preventing privacy leaks, performing data auditing, and guaranteeing high availability in the face of various security attacks.On the other hand, should the task of addressing these security issues be solely placed on the shoulders of the cloud service providers, or indeed should both the service providers and the service users be responsible for this task?A number of studies have been carried out that investigate the fundamental properties of cloud security issues, including data auditing, searchable data encryption, hypervisor protection, cloud forensics, and disaster recovery, to name but a few.In fact, cloud security is driving how we define and develop cloud computing solutions.The objective of this special issue is to provide a forum for researchers working on cloud security to present their recent research results.This special issue attracted 58 submissions of high quality research from around the world.Through a rigorous review process, the following 10 papers were selected for publication.These papers present results of analysis, experimentation, simulation, advanced theories, and system implementation.More specifically, they cover the topics of Operating System (OS) Fingerprinting, Side-Channel Attacks, Attribute-Based Signatures (ABSs), Fuzzy Authorization for Cloud Storage, Secure Software-Defined Network (SDN) Architecture for Cloud, Self-Destructing Data, Secure Group Data Sharing, Data Access Control for Peer-to-Peer Storage Cloud, SQL Operations on Encrypted Data, and Linear Regression

The articles in this special section focus on new applications that are supported by cloud computing.

Vehicular communication networks are increasingly being considered as a means to conserve fuel and reduce emissions within transportation systems. This paper focuses on using traffic light signals to communicate with approaching vehicles. The communication can be traffic-light-signal-to-vehicle (TLS2V) and vehicle-to-vehicle (V2V). Based on the information sent, the vehicle receiving the message adapts its speed to a recommended speed (SR), which helps the vehicle reduce fuel consumption and emissions. The key contribution of this paper is the proposal of a comprehensive optimization model that involves V2V and TLS2V communications. The objective function is to minimize fuel consumption by and emissions from vehicles. The speed that can achieve this goal is the optimum SR(SR*). We also propose efficient heuristic expressions to compute the optimum or near-optimum value of SR.

Delay tolerant networks (DTN) are characterized by a lack of continuous end-to-end connections due to node mobility, constrained power sources, and limited data storage space of some or all of its nodes. To overcome the frequent disconnections, DTN nodes are required to store data packets for long periods of time until they come near other nodes. Moreover, to increase the delivery probability, they spread multiple copies of the same packet on the network so that one of them reaches the destination. Given the limited storage and energy resources of many DTN nodes, there is a tradeoff between maximizing delivery and minimizing storage and energy consumption. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the presence of a global observer that can collect information about all the nodes in the network. Next, we propose a new protocol based on social grouping among the nodes to maximize data delivery while minimizing network overhead by efficiently spreading the packet copies in the network. We compare the new protocol with the optimal results and the existing well-known routing protocols using real life simulations. Results show that the proposed protocol achieves higher delivery ratio and less average delay compared to other protocols with significant reduction in network overhead.

Multicopy routing strategies have been considered the most applicable approaches to achieve message delivery in Delay Tolerant Networks (DTNs). Epidemic routing and two-hop forwarding routing are two well-reported approaches for delay tolerant networks routing which allow multiple message replicas to be launched in order to increase message delivery ratio and/or reduce message delivery delay. This advantage, nonetheless, is at the expense of additional buffer space and bandwidth overhead. Thus, to achieve efficient utilization of network resources, it is important to come up with an effective message scheduling strategy to determine which messages should be forwarded and which should be dropped in case of buffer is full. This paper investigates a new message scheduling framework for epidemic and two-hop forwarding routing in DTNs, such that the forwarding/dropping decision can be made at a node during each contact for either optimal message delivery ratio or message delivery delay. Extensive simulation results show that the proposed message scheduling framework can achieve better performance than its counterparts.

This paper introduces a novel multicopy routing protocol, called Self-Adaptive Utility-based Routing Protocol (SAURP), for Delay Tolerant Networks (DTNs) that are possibly composed of a vast number of devices in miniature such as smart phones of heterogeneous capacities in terms of energy resources and buffer spaces. SAURP is characterized by the ability of identifying potential opportunities for forwarding messages to their destinations via a novel utility function-based mechanism, in which a suite of environment parameters, such as wireless channel condition, nodal buffer occupancy, and encounter statistics, are jointly considered. Thus, SAURP can reroute messages around nodes experiencing high-buffer occupancy, wireless interference, and/or congestion, while taking a considerably small number of transmissions. The developed utility function in SAURP is proved to be able to achieve optimal performance, which is further analyzed via a stochastic modeling approach. Extensive simulations are conducted to verify the developed analytical model and compare the proposed SAURP with a number of recently reported encounter-based routing approaches in terms of delivery ratio, delivery delay, and the number of transmissions required for each message delivery. The simulation results show that SAURP outperforms all the counterpart multicopy encounter-based routing protocols considered in the study.

In spite of the dramatic growth in the number of smartphones in recent years, the challenge of limited energy capacity of these devices has not been solved satisfactorily. However, in the era of cloud computing, the limitation on energy capacity can be eased off in an efficient way by offloading heavy tasks to the cloud. It is important for smartphone and cloud computing developers to have insights into the energy cost of smartphone applications before implementing the offloading techniques. In this paper, we evaluate the energy cost of multimedia applications on smartphones that are connected to Multimedia Cloud Computing (MCC). We have conducted an extensive set of experiments to measure the energy costs to investigate whether or not smartphones save energy by using MCC services. In other words, we investigate the feasibility of MCC to provide the Energy-as-a-Service (EaaS). Specifically, we compared the energy costs for uploading and downloading a video file to and from MCC with the energy costs of encoding the same video file on a smartphone. The aforementioned comparison was performed by using HTTP and FTP Internet protocols with 3G and WiFi network interfaces. All the experiments were conducted on an Android based HTC Nexus One smartphone. Our results show that MCC provides the smartphones with many multimedia functionalities and saves smartphone energy from 30% to 70%.

In this paper, we propose a new routing protocol for home networks, called dependable routing protocol (DRP) that adapts to the changes in local topology within home networks environments. DRP is based on an effective selection of paths through which a packet must pass to reach the home unit. The selection, in such dynamic home networks, is made using dependable routing, i.e, in a way that maximizes the routes quality between the network nodes and the home unit while minimizing the Failure of Service (FoS). To minimize FoS, DRP maintains requirements on both the tolerable end-to-end delay (for time-sensitive routing) and the bit error rate (for reliable routing) within the network. To achieve this, we formulate the routing dependability problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the route quality as well as the delay and bit error rate of a route in a home network scenario are derived. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the home network performance when compared to one prominent routing protocol: the Minimum Total Transmission Power Routing scheme, MTPR.

Inter-contact times (ICTs) between moving vehicles are one of the key metrics in vehicular networks, and they are also central to forwarding algorithms and the end-to-end delay. Recent study on the tail distribution of ICTs based on theoretical mobility models and empirical trace data shows that the delay between two consecutive contact opportunities drops exponentially. While theoretical results facilitate problem analysis, how to design practical opportunistic forwarding protocols in vehicular networks, where messages are delivered in carry-and-forward fashion, is still unclear. In this paper, we study three large sets of Global Positioning System (GPS) traces of more than ten thousand public vehicles, collected from Shanghai and Shenzhen, two metropolises in China. By mining the temporal correlation and the evolution of ICTs between each pair of vehicles, we use higher order Markov chains to characterize urban vehicular mobility patterns, which adapt as ICTs between vehicles continuously get updated. Then, the next hop for message forwarding is determined based on the previous ICTs. With our message forwarding strategy, it can dramatically increase delivery ratio (up to 80%) and reduce end-to-end delay (up to 50%) while generating similar network traffic comparing to current strategies based on the delivery probability or the expected delay.

In contention-based wireless networks, packet collisions are considered the main source of data loss. Retransmission of the lost packet is done several times until an acknowledgment of successful reception (ACK) is received or the maximum number of retries is reached. The retransmission delay is drawn randomly from an interval, called the Backoff Interval. A good choice the backoff interval reduces the number of collisions and, therefore, increases the throughput and decreases the energy consumed in retransmissions. In this paper, we propose a backoff scheme based on fuzzy logic. The new scheme depends on locally measured data to estimate the backoff interval which supports the distributed nature of the vehicular networks. We present several versions of the Fuzzy Backoff scheme and compare them with other known schemes: the binary exponential backoff (BEB), and an optimal scheme which requires the knowledge of the total number of nodes in the network. We used throughput, fairness, and energy consumption as performance measures for evaluation. Results show an improvement of the fuzzy-based schemes compared to the BEB, and approaching the optimal results.

Smartphones equipped with latest applications and features are the reality of the modern telecommunication world. Researchers have studied the energy consumption in smartphones while running some network related applications (NRAs) but they have neither fully covered the wide pool of NRAs nor provided a methodology to measure the energy consumption in smartphones. In this paper, we identified the most popular NRAs and configurable parameters which can impact the energy consumption while running these NRAs. We further propose a methodology to measure the energy consumption in smartphones while conducting a feasible set of experiments. We present a measurement bench for measuring the energy consumption in smartphones. We conducted selected experiments on latest smartphones (BB 9700, Nokia E71, HTC Nexus One and HTC HD2) to support our methodology. Our methodology evaluates the impact of configurable parameters and NRAs on energy consumption and provide a base to compare the energy consumption across smartphones.

With the tremendous growth in mobile applications, communication accounts for a significant portion of a smartphone's total energy consumption. We studied the traffic pattern of smartphones and observed that a good portion of the packets are of small size and the generated traffic is bursty in nature. Motivated by these observations, we propose a Low Energy Data-packet Aggregation Scheme (LEDAS) in this paper. It accumulates a number of upper layer packets into a burst at medium access control (MAC) level, based on accumulation time, size, and number of packets. With this scheme, larger bursts lead to longer inactivity periods during which the communication module can be kept in doze mode. In addition, fewer MAC frames lead to less overheads and contentions in the wireless medium. However, the data packets incur delays due to the accumulation process. We have given a detail flowchart description of the technique. By means of analysis, we have derived expressions for the average values of burst size, burst inter-arrival times, and number of packets in a burst. We also evaluated the efficacy of the technique by simulations and showed the energy-delay trade-offs. Finally, we explained a test-bench to evaluate the energy saving potential of LEDAS on a smartphone.

Peer-to-peer networks offer advantages over traditional client-server networking models, such as the lack of a need for connectivity to trusted intermediary hosts or servers and the use of less costly communication links. While they have become popular in the wired broadband environment, they have not yet been effectively adapted to the resource-constrained mobile network environment. They promise significant potential in applications such as the sharing of files like multimedia and operating system updates between mobile devices. However, the peer-to-peer model faces unique challenges in the mobile context, such as limitations on processing power, on-board device memory, wireless data bandwidth, and available battery energy. We propose a high-level framework for a peer-to-peer protocol with these specific constraints addressed. In addition, we investigate the feasibility of a practical implementation of a peer-to-peer file sharing model on smartphones, including an analysis of how performance is impacted by various variables that can be dynamically controlled in the protocol. Through experimentation on leading smartphones, we have found various optimal strategies, including minimizing the upload-to-download ratio to conserve battery life, using larger file segments to increase throughput, and using sockets to decrease memory overhead.

Smartphones are constrained in battery-energy and resources such as processing power, storage, and Internet bandwidth. The capabilities of the smartphones can be enhanced if they can access the hardware, software resources and data on a laptop in an energy efficient way. This enables the owner of a smartphone and a laptop to use his/her devices with flexibility. With this view, we propose Universal Computing and Communication Interface (UCCI) to facilitate such sharing of resources between two wireless portable devices using wireless personal area network (WPAN). We developed prototypes of UCCI using BlackBerry 9700 and HTC Nexus One smartphones, and performed experiments on an energy measurement testbed. We show the efficacy of the proposed model through experiments, and evaluated the energy costs of an application which transfers file through 3G, WiFi and Bluetooth links. We also measured the costs of file compression and decompression on a HTC Nexus One smartphone, and discuss how they affect the file transfer costs.

Networks in which nodes are intermittently connected, and have limited storage space and power, are termed Delay Tolerant Networks (DTN). To overcome these conditions, DTN routing protocols require nodes to store data packets for long periods of time until they contact with each other. In addition, they spread multiple copies of the same packet in the network to increase the probability of one of them reaching the destination. Long-term storage and multiple transmissions require large buffer space and non-restricted power availability which is hard to exist in DTN. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the knowledge of present and future nodes contact and buffer space. After that, we analyze the previously developed heuristic protocols, and we propose a new protocol based on social relations between the nodes to avoid redundant copying of packets. Simulation results show that the proposed protocol significantly reduces energy consumption and provides better delivery ratio compared to other protocols.

Mobile Ad Hoc networks (MANETs) are gaining increased interest due to their wide range of potential applications in civilian and military sectors. The self-control, self-organization, topology dynamism, and bandwidth limitation of the wireless communication channel make implementation of MANETs a challenging task. Vehicular Ad Hoc Networks (VANETs) are a special kind of MANETs that aims at providing communications among vehicles on the roads. The fact that a vehicle's movement is predictable and constrained by the road trajectory is a feature that gives VANETs their distinction. The Connected Dominating Set (CDS), a.k.a. virtual backbone or spine, has been proposed to facilitate routing, broadcasting, and establishing a dynamic infrastructure for distributed location databases in MANETs and VANETs. Minimizing the CDS cardinality simplifies the network abstracted topology, allows for using shorter routes, and reduces the number of required retransmissions in broadcasting scenarios. Due to the fact that minimizing the CDS size is NP-complete problem, approximation algorithms and heuristics have been used to reduce the CDS size. Localized CDS creation algorithms proved to run fast and to generate light signaling overhead. Computationally, the simplest localized CDS creation algorithms is Wu and Li algorithm; however, this algorithm is characterized by a relatively high signaling overhead. To reduce the signaling overhead of Wu and Li algorithm, a modified version of the algorithm is proposed; this version is built on the assumption that utilizing the location information of network members reduces the signaling overhead. In this paper, we claim that utilizing location information does not guarantee signaling overhead reduction and it may increase it; moreover, we introduce some modifications that guarantee overhead reductMobile Ad Hoc networks (MANETs) are gaining increased interest due to their wide range of potential applications in civilian and military sectors. The self-control, self-organization, topology dynamism, and bandwidth limitation of the wireless communication channel make implementation of MANETs a challenging task. Vehicular Ad Hoc Networks (VANETs) are a special kind of MANETs that aims at providing communications among vehicles on the roads. The fact that a vehicle's movement is predictable and constrained by the road trajectory is a feature that gives VANETs their distinction. The Connected Dominating Set (CDS), a.k.a. virtual backbone or spine, has been proposed to facilitate routing, broadcasting, and establishing a dynamic infrastructure for distributed location databases in MANETs and VANETs. Minimizing the CDS cardinality simplifies the network abstracted topology, allows for using shorter routes, and reduces the number of required retransmissions in broadcasting scenarios. Due to the fact that minimizing the CDS size is NP-complete problem, approximation algorithms and heuristics have been used to reduce the CDS size. Localized CDS creation algorithms proved to run fast and to generate light signaling overhead. Computationally, the simplest localized CDS creation algorithms is Wu and Li algorithm; however, this algorithm is characterized by a relatively high signaling overhead. To reduce the signaling overhead of Wu and Li algorithm, a modified version of the algorithm is proposed; this version is built on the assumption that utilizing the location information of network members reduces the signaling overhead. In this paper, we claim that utilizing location information does not guarantee signaling overhead reduction and it may increase it; moreover, we introduce some modifications that guarantee overhead reduction. we conduct extensive simulations to investigate the correctness of our claim, and we study the impact of using location information on the run time and the size of the CDS.ion. we conduct extensive simulations to investigate the correctness of our claim, and we study the impact of using location information on the run time and the size of the CDS.

A Cognitive Radio (CR) network seeks to access and utilize the unused license spectrum portions. These portions have dynamic behavior that introduces challenge for the CR user to allocate and rendezvous on the same portion. In CR Ad-hoc Network (CRAHN), there is difficulty of maintaining a Common Control Channel (CCC); for that, CRAHN requires a protocol able to guarantee users rendezvous without CCC. This paper proposes a distributed Medium Access Control (MAC) protocol using the concept of Balanced Incomplete Block Design (BIBD) to achieve a rendezvous channel without a CCC or synchronization. If the channels of a network are assigned to the BIBD elements and the searching sequence to the BIBD block, there is a guarantee of a rendezvous in at least one channel for each searching sequence. Simulation results confirm that the protocols outperform other protocols with respect to Time to Rendezvous (TTR), channel utilization, network throughput, and percentage of collisions between the SU and the PU network. In addition, the protocols fairly distribute the network load on channels, and share the channels fairly among network nodes.

The increased usage of mobile devices and the scarce, regulated radio resources in wireless networks present a challenge to efficiently allocate channels to users. Existing algorithms designed to assign channels in a network range from dynamic channel allocation (DCA) algorithms to fixed channel allocation (FCA) algorithms - each with their own advantages and drawbacks. In this paper, we propose a fuzzy-based hybrid channel assignment algorithm that is adaptive to the traffic conditions of the network by employing the frequency and time division multiple access (FDMA/TDMA) FCA algorithm in low traffic conditions and the Geometric DCA algorithm in high traffic conditions. The switching mechanism employs fuzzy logic and known traffic patterns. This approach is aimed at reducing the overall signaling cost of solely utilizing the DCA strategy while maintaining a comparable failure rate. Simulation results show that the proposed hybrid algorithm reduces the signaling cost of DCA algorithm by about 20-30% while achieving similar failure rates to the Geometric DCA.

In this paper, we propose a new intersection-based geographical routing protocol, called delay tolerant routing protocol (DTRP) that adapts to the changes in the local topology within city environments. DTRP is based on an effective selection of road intersections through which a packet must pass to reach the gateway to the Internet. The selection, in such delay tolerant VANETs, is made in a way that maximizes the connectivity probability of the route between mobile nodes and the gateway while maintaining a threshold for the end-to-end delay and the hop count within the network. To achieve this, we formulate the QoS routing problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the connectivity probability as well as the delay and hop count of a route in a two-way road scenario are derived. Then, we propose a genetic algorithm to solve the optimization problem. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the VANETs performance when compared with several prominent routing protocols, such as GPSR, GPCR and OLSR.

In sparse mobile networks, nodes are connected at discrete periods of time. This disconnection may last for long periods in suburban and rural areas. In addition, mobile nodes are energy and buffer sensitive, such as in mobile sensor networks. The limited power and storage resources, combined with the intermittent connection have created a challenging environment for inter-node networking. This type of networks is often referred to as Delay Tolerant networks (DTN). Routing protocols developed for DTN focused on minimizing the end-to-end delay as a means of maximizing number of delivered packets. Therefore, they tend to spread many copies of the same packet into the network, assuming the availability of sufficient storage space and power. A key factor to help maintain a clean environment, is the reduction of energy consumption which can be achieved by decreasing number of transmissions in the network. In this paper, we formulate a mathematical model for optimal routing in DTN to minimize number of transmissions. In addition, we study and analyze the DTN heuristic routing protocols. After that, we propose an eco-friendly routing protocol, EFR-DTN, that efficiently uses simple information provided from the network to deliver packets with higher delivery ratio and minimum energy consumption than the other protocols. Simulation results show the outperformance of the proposed protocol under different buffer capacities, traffic loads, packet TTL values, and number of nodes in the network.

This paper introduces a novel routing protocol in Delay Tolerant Networks (DTNs), aiming to solve the online distributed routing problem. By manipulating a collaborative reinforcement learning technique, a group of nodes can cooperate with each other and make a forwarding decision for the stored messages based on a cost function at each contact with another node. The proposed protocol is characterized by not only considering the contact time statistics under a novel contact model, but also looks into the feedback on user behavior and network conditions, such as congestion and buffer occupancy sampled during each previous contact with any other node. Therefore, the proposed protocol can achieve high efficiency via an adaptive and intelligent routing mechanism according to network conditions. Extensive simulation is conducted to verify the proposed protocol, where a comparison is made with a number of existing encounter-based routing protocols in term of the number of transmissions of each message, message delivery delay, and delivery ratio. The results of the simulation demonstrate the effectiveness of the proposed technique.

Abstract In this paper, we propose a finite‐state Markov model for per‐user service of an opportunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading channels as finite‐state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a finite‐state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In this paper, a rate‐based admission control scheme for a single shared wireless base station with opportunistic scheduling and adaptive modulation and coding (AMC) is proposed. The proposed admission scheme maintains minimum average rates of the admitted users, i.e., new users will be admitted if the base station has enough resources to support the required minimum average transmission rates of all users. The proposed scheme relies on an analytical model for the average per‐user rates of an opportunistic scheduling in an unsaturated scenario, where some queues may be empty for certain periods of time. We provide extensive simulation results to demonstrate the accuracy of the base analytical model on which our admission scheme relies. Copyright © 2009 John Wiley & Sons, Ltd.

In contention-based wireless networks, collisions between data packets can be reduced by introducing a random delay before each transmission. Backoff schemes are those that provide the backoff interval from which the random delay is drawn. In this paper, we propose a new scheme which calculates the backoff interval dynamically according to the network conditions. The network conditions are measured locally by each node, which supports the distributed nature of the vehicular networks. The measures are used by a fuzzy inference system to calculate the backoff interval. We compare the proposed scheme with other known schemes: the binary exponential backoff (BEB), the sensing backoff algorithm (SBA) and an optimal scheme which requires the knowledge of the number of nodes in the network (Genie). The evaluation measures are the throughput and fairness. Results show an improvement of the fuzzy-based schemes compared to the BEB and SBA, especially for large number of nodes in the network.

Dissemination of data related to dynamic and timely traffic / road condition, and any unexpected events can significantly improve the quality of driving with respect to time, distance, and safety. Although extending region of data dissemination in the network constructed among vehicles informs more vehicles on road network, it increases communication cost and imposes delay on the network. The imposed delay has an undesirable impact on vehicle safety applications which require very low message latencies. This research investigates how communication cost and additional travel cost are affected while the region of dissemination is increased. Moreover, this research aims at studying the effective region for data dissemination in an ad-hoc network among vehicles. The simulation results indicate that extending the region of data dissemination increases the communication cost and decreases the additional travel cost. Also, there is an optimal region for data dissemination so that effective propagation of data to a certain area reduces the additional travel cost.

In this paper, we consider maximum rate opportunistic scheduling from a single wireless base station with a single antenna to multiple mobile users, each equipped with a single antenna. We show that a finite-state Markovian model can capture the dynamics of a single user's service, namely peruser service. We consider a saturated scenario, where the base station always has buffered data for transmission.

A vehicular ad hoc network (VANET) is a network of intelligent vehicles that communicate with other vehicles in the network. The main objective of VANET is to provide comfort and safety for passengers. In addition, various transaction based services, such as information about gas prices, restaurant menu, and discount sale, can be provided to drivers. In order to make these services available, there is a need for an efficient service discovery protocol. In this paper, we propose a new protocol called Address Based Service Resolution Protocol (ABSRP) to discover services in vehicular ad-hoc networks. As most of the transaction based services are provided by roadside units, we exploit their presence to perform service discovery. We utilize the unique address assigned to each service provider in order to discover a route to that service provider. Our technique proactively distributes the service provider's address along with its servicing capabilities to other roadside units within a particular area. Each roadside unit will then utilize this information to service the request placed by the vehicles. If the service provider (destination node) is not reachable over the vehicular network, we propose to use a backbone network to service requests. Our approach is independent of the network layer routing protocol. We have evaluated the performance of our approach by using the Qualnet simulation tool.

Extending the battery life of portable wireless devices has been in the focus of researchers for close to a decade. Several energy management techniques have been investigated at different levels of system design -- starting from silicon at the bottom to application design at the top, with communication protocols and operating system in between. In this paper, we present a model to estimate the energy cost of an application running on a portable wireless device. To develop the cost model, we partition a wireless device into two components, namely, computation and communication. Each component is modeled by a state-transition diagram. Two attributes are associated with each state: an average power cost and a state residence time. The cost of each state of the state-transition diagrams is validated by actual measurements. For a constant voltage supply, the average power cost of a state is denoted by the average current drawn by the component. The state residence times are estimated from the behavior of applications. The cost model has been validated by performing actual measurement of energy cost. We find that the estimated cost and the actual energy cost are within 5-10% of each other. This study will help us in improving the design of energy efficient software for portable devices. Moreover, the energy consumption breakdown into components will be an essential guide for future research in energy management of hardware and software systems.

In infrastructure wireless networks, nodes at the edge of the coverage area need to spend more energy to transmit their packets than those close to the Access Point (AP). Less energy is required if intermediate nodes can be used to forward data. To enable this, intermediate nodes need an incentive and there must be a mechanism for selecting these nodes. In this paper we propose Energy-aware Co-Operative (ECO) relaying for selecting relays to forward packets. The technique is based on the idea of Relative Energy Usage (REU), which reflects the proportion of energy that a node saves by forwarding its packets through relays. A node which saves more energy by using relays is more likely to be chosen as a relay. Conversely, nodes are only permitted to use relays proportionate to the amount of energy they themselves have spent as relays. We compare our scheme with direct transmission, minimum energy path (MnEP), and maximum residual energy path (MxRE). We show that ECO can transmit 50% more data than direct transmission, while using less energy on average. Although MnEP and MxRE can also transmit more data than direct transmission, they do so at severe energy cost to a small number of nodes, doubling the average energy usage, making them ill-suited to commercial networks.

The authors propose a joint channel and queue-aware scheduling scheme for the downlink of the IEEE 802.16 wireless metropolitan area networks (WMANs). The base station scheduler determines the order of transmissions based on the channel quality and the stability status of each base station queue. Stability status is defined as the ratio of the average departure rate to the average arrival rate for each queue. The integration of opportunistic and stable service deliveries in the scheduling scheme increases the throughput and reduces the dropping probability and service discontinuity for non-real-time applications. Simulation results are given to demonstrate the performance of the proposed scheduling scheme.

An efficient anonymous communication protocol, called MANET Anonymous Peer-to-peer Communication Protocol (MAPCP), for P2P applications over mobile ad-hoc networks (MANETs) is proposed in this work. MAPCP employs broadcasts with probabilistic-based flooding control to establish multiple anonymous paths between communication peers. It requires no hop-by-hop encrypt ion/decryption along anonymous paths and, hence, demands lower computational complexity and power consumption than those MANET anonymous routing protocols. Since MAPCP builds multiple paths to multiple peers within a single query phase without using an extra route discovery process, it is more efficient in P2P applications. Through analysis and extensive simulations, we demonstrate that MAPCP always maintains a higher degree of anonymity than a MANET anonymous single-path routing protocol in a hostile environment. Simulation results also show that MAPCP is resilient to passive attacks.

The twenty-one papers in this special issue are devoted to peer-to-peer communications and their applications. Covers such topics as: overlay networks, searching, video streaming, files and servers, and theories and applications.

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the gradient boundary detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals.

Abstract Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. In this paper, we propose two packet scheduling schemes exploiting base station selection diversity in the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous channel state information (CSI) to select the best user from the best serving base station at each time slot. This technique increases the system throughput by increasing multiuser diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. In addition, we investigate fairness issue of wireless scheduling schemes. Due to different characteristics of wireless scheduling schemes, the existing fairness indexes may result in misleading comparison among different schemes. We propose a new fairness index to compare the overall satisfaction of the network users for different scheduling schemes. Copyright © 2006 John Wiley & Sons, Ltd.

An efficient anonymous communication protocol, called MANET anonymous peer-to-peer communication protocol (MAPCP), for P2P applications over mobile ad-hoc networks (MANETs) is proposed in this work. MAPCP employs broadcasts with probabilistic flooding control to establish multiple anonymous paths between communication peers. It requires no hop-by-hop encryption/decryption along anonymous paths and, hence, demands lower complexity of computation and power consumption than other anonymous routing protocols for MANETs. Since MAPCP builds multiple paths to multiple peers within a single query phase without using an extra route discovery process, it is more efficient in P2P applications. Through analysis and extensive simulations, we demonstrate that MAPCP always maintains a higher degree of anonymity than a MANET anonymous single-path routing protocol in a hostile environment. Simulation results also show that MAPCP is resilient to passive attacks in data forwarding for both one-to-one and one-to-many communications.

In this paper, we propose two analytical models for per-user throughput of an opportunistic scheduling scheme over a broadcast fading channel. For the first model, we use a piecewise linear approximation of the achievable transmission rates versus the values of Signal to Noise and Interference Ratio (SINR). We obtain the conditional average transmission rate of a mobile station, given the maximum channel quality of the other competing mobile stations. Using the probability distribution function of the maximum channel quality of the competing mobile stations, we obtain a closed form unconditional average transmission rate, i.e., per-user throughput, of a mobile station. For the second model, we use a similar approach, but with a precise model of the achievable rates. Furthermore, statistically nonidentical channels for different mobile stations are considered. Thus, the second model is more general and provides more accurate solution, but it requires more computations. The proposed models are useful for call admission control as well as performance studies of wireless networks. Simulation results are given to demonstrate the accuracy of the proposed analytical models.

In this paper, we propose a novel peer-to-peer architecture for wireless mobile networks where a cross-layered gossip-like protocol is the heart of the architecture. The goal of this architecture is to reduce the bandwidth consumption and at the same time, to provide more user participation flexibility. Simulation results are given to demonstrate the performance of the proposed peer-to-peer architecture.

Geocasting in wireless sensor networks and ad hoc networks is the delivery of a message from a source to all the nodes in a given geographical region. The objectives of a geocasting protocol are two-folds: guaranteed message delivery and low transmission cost. Most of the existing protocols do not guarantee message delivery, and those that do incur a high transmission costs. In this paper, we introduce the idea of a virtual surrounding face (VSF), and present a geocasting protocol based on VSF. By using mathematical analyses and simulation studies, we show that the proposed protocol guarantees message delivery and has a significant lower transmission cost than the existing approaches.

In Bluetooth network environment, device discovery is achieved by the master running the Inquiry protocol and slaves running the Inquiry Scan protocol. The master and a slave establish a connection using the Page and Page Scan protocols, respectively. Once a connection is established between a master and a slave, upper level data can be exchanged between the two devices. Thus, device discovery and connection establishment are fundamental to communication between two Bluetooth devices. While a device is in the process of discovering other devices, there is a random delay. We analyze the discovery time for different situations. We further show the delay-distribution for varied number of devices. We explain reasons of occasional long discovery time, and propose means to improve it.

In this paper, we propose a novel scheduling scheme for the downlink of IEEE 802.16 networks. A scheduler at the Base Station (BS) decides the order of downlink bursts to be transmitted. The decision is made based on the quality of the channel and the history of transmissions of each Subscriber Station (SS). The scheduler takes advantage of temporal channel fluctuations to increase the BS's throughput and maintain fairness by balancing the long term average throughput of SSs. Simulation results are given to demonstrate the performance of the proposed scheduling scheme.

Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. Exploiting base station selection diversity, in this paper, we propose two scheduling schemes for the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous Channel State Information to transmit to the best user from the best serving base station in each time slot. This technique increases the system throughput by increasing multi-user diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. We also investigate the issue of fairness analysis of wireless scheduling schemes. Due to the unique characteristics of wireless scheduling schemes, the existing fairness indexes fail to provide a proper comparison among different scheduling schemes. We propose a new fairness index to compare the overall satisfaction of the network users among different wireless scheduling schemes. This approach complies with the definition of max-min fairness which is a widely accepted notion of fairness for data communication networks.

In this paper, we propose a novel energy-efficient approach, a localized routing tree (LRT) coupled with a route redirection (RR) strategy, to support various types of queries. LRTs take care of the sensors near the sink and reduce the energy consumption of these sensors, and RR reduces the energy cost of data receptions. Compared to the existing approaches, simulation studies show that LRT together with RR has significant improvement on the query capacity.

In wireless channels, the bursty nature of block errors render immediate packet retransmissions at the link level ineffective. Cooperative communication is a promising technique to combat the negative impacts of channel fading by providing diverse channels between peers in wireless ad-hoc networks. In this paper, an analytical model is proposed for the throughput of the node cooperative automatic repeat request scheme for wireless ad-hoc networks. The model is based on a two-state Markov model for block errors in the wireless fading channels. Simulation results are given to demonstrate effectiveness of the analytical model

In this paper, we investigate the measurement of fairness, discuss well known fairness notions, and propose a new utility-based framework to evaluate the degree of fairness of resource allocation schemes in wireless access networks. The proposed framework has certain desirable features. It offers clear definitions and relevant methodology, takes into account both effort and service unfairness, and can be customized for different application types with different QoS requirements. Numerical examples and case studies are given to demonstrate the effectiveness of the proposed framework.

In a Bluetooth piconet, the Master essentially controls the channel. Due to an absence of coordination between independent Masters while accessing the wireless medium, devices will encounter high packet interference if several piconets are simultaneously operating in the same area. Since even a headset and a mobile phone can be connected with a Bluetooth link forming a piconet, it may not be unusual to find tens of independent piconets in crowded places like airports, international conferences, shopping malls, and so on. Study of packet interference is important because interference affects the throughput of a piconet. Motivated by the fact that applications will benefit, in terms of higher available data rate in one direction, by using multiple-slot packets in an asymmetric manner, in this paper, we present an analytical model of packet interference in a cluster of piconets using multiple-slot packets. Also, considering that all the portable devices can have a Bluetooth interface and people are highly mobile these days, it will not be uncommon to find a cluster of piconets of both the 79-hop and the 23-hop types in the same area. We then present an analytical model of interference of multiple-slot packets in a heterogeneous cluster of Bluetooth piconets. By a heterogeneous cluster we mean some piconets are of the 23-hop type and the rest are of 79-hop type. We show how the aggregate throughput in a cluster of piconets degrade under various traffic scenarios, such as 1-slot, 3-slot, and 5-slot packets in symmetric and asymmetric modes in synchronous and asynchronous conditions of Master clocks. Our analytic model is based on the idea of probabilistic graphs, where a node denotes a piconet and an edge denotes the probability of interference between two nodes. Though the 23-hop system has been phased out, our work gives a general approach to model packet interference in multiple, frequency-hopping systems that need not be Bluetooth systems.

Study of packet interference is important because interference affects the throughput of a piconet. Motivated by the fact that applications will benefit, in terms of higher available data rate in one direction, by using multiple-slot packets in an asymmetric manner, in this paper, we present an analytical model of packet interference in a cluster of piconets using multiple-slot packets. Also, considering that all the portable devices can have a Bluetooth interface and people are highly mobile these days, it will not be uncommon to find a cluster of piconets of both the 79-hop and the 23-hop types in the same area. We then present an analytical model of interference of multiple-slot packets in a heterogeneous cluster of Bluetooth piconets. By a heterogeneous cluster we mean some piconets are of the 23-hop type and the rest are of 79-hop type. We show how the aggregate throughput in a cluster of piconets degrade under various traffic scenarios, such as 1-slot, 3-slot, and 5-slot packets in symmetric and asymmetric modes in synchronous and asynchronous conditions of master clocks. Our analytic model is based on the idea of probabilistic graphs, where a node denotes a piconet and an edge denotes the probability of interference between two nodes.

Hierarchical routing is an effective way to solve the scalability problem in flat networks. The optimal total routing table (RT) size is affected by four parameters: the total number of nodes in a network hierarchy levels, the number of clusters at each level, and cluster size distribution. An optimal solution of total RT size was given in L. Kleinrock et al. (1977). However, the optimal solution was based on a major assumption: all nodes in the network have equal size RTs. It is not clear whether the optimal results stated in L. Kleinrock et al. (1977) still hold without this assumption. We provide the general integer solution of optimal RT sizes without this assumption. In addition, we will show how the total number of nodes, the number of hierarchical levels, and the number of the highest-level clusters affect the total RTsize. Moreover, some important properties of the two-level cluster structure are extensively addressed, namely the impact of the variance of cluster size distribution on intra-cluster update costs and the RTsize.

In this paper, we present a reservation-based medium access control (MAC) protocol with multicast support for wavelength-division multiplexing networks. Our system is based on the single-hop, passive optical star architecture. Of the available wavelengths (channels), one channel is designated as a control channel, and the remaining channels are used for data transmission. Each node is equipped with a pair of fixed transceiver to access the control channel, and a fixed transmitter and a tunable receiver to access data channels. For easy implementation of the protocol in hardware and for precisely computing the protocol's processing overhead, we give a register-transfer model of the protocol. We simulate the protocol to study its throughput behavior, and present its analytic model. For a node to be able to send data packets in successive data slots with no time gap between them, in spite of the situation that the protocol's execution time may be longer than data transmission time, we propose the idea of multiple MAC units at each node. Unicast throughput of our protocol reaches the theoretically possible maximum throughput for MAC protocols with distributed control, and the multicast throughput is at least as good as, and even better than, those delivered by existing MAC protocols with distributed control.

The desired properties of a medium access control (MAC) protocol in mobile ad hoc network (MANET) include: (1) meet quality of service (QoS) requirements for real-time nodes, (2) be decentralized, (3) achieve fairness from viewpoint of throughput or energy consumption, and (4) be immune to the hidden node problem. Though there have been numerous proposed MAC protocols for the IEEE 802.11 WLAN, few of them possess all of the four properties mentioned above. Our protocol can support real-time traffic and satisfy QoS requirements, and can achieve fairness among non-real-time nodes. Also, without using any centralized control, it can be easily deployed in MANET. An analytic model of the protocol's throughput has also been developed. We compare the protocol's throughput obtained from its analytic model and simulation to validate each other.

Hierarchical routing is used to reduce routing update costs, and clustering algorithms partition networks into multilevel cluster structures. In hierarchical networks, routing performance is greatly affected by the sizes and structures of partitioned clusters. Two existing clustering algorithms are based on the concepts of the lowest ID and the maximum degree of nodes. In this paper, we present a new clustering algorithm based on the concept of the variable degree of nodes. Our new algorithm produces clusters with the low cluster variance in size, and the number of the clusters is close to the optimal value that leads to the smallest total routing table size. The performance of the algorithm is evaluated by using simulated ad hoc networks.

We propose a reservation based multicast protocol for the single-hop passive optical star network. Of the available wavelengths (channels), one channel is designated as a control channel, and the remaining channels are used for data transmission. A node accesses the control channel using a fixed transmitter and a fixed receiver. A node sends data packets using a fixed transmitter and receives packets through a tunable receiver (filter). All the channels are viewed as sequences of frames. In addition, frames of the control channel are further divided into mini slots. Corresponding to each node in the network, there is a mini slot in a control frame. A node puts its multicast request in its designated mini slot in a control frame. At the end of a control frame, all nodes receive the multicast requests of all other nodes, and decide which nodes are going to transmit and/or receive during the following data slot. An easily implementable way of resolving destination and source conflicts is presented. We simulate the protocol to study its throughput behavior, and present its analytic model. Simulation results show that our protocol delivers maximum unicast throughput, and the protocol's multicast throughput is much better than existing protocols using a control channel.

In this paper, we first show that the degree four Cayley graph proposed in a paper appearing in the January 1996 issue of IEEE Transactions on Parallel and Distributed Systems is indeed isomorphic to the wrapped butterfly. The isomorphism was first reported by Muga and Wei in the proceedings of PDPTA '96. The isomorphism is shown by using an edge-preserving bijective mapping. Due to the isomorphism, algorithms for the degree four Cayley graph can be easily developed in terms of wrapped butterfly and topological properties of one network can be easily derived in terms of the other. Next, we present the first optimal oblivious one-to-one permutation routing scheme for these networks in terms of the wrapped butterfly. Our algorithm runs in time O(/spl radic/N), where N is the network size.

We present the object oriented design of a software system called NETMAN to experiment distributed algorithms on a large network of workstations. The design has been structured into three layers and a central monitoring site. We have implemented the system in Java. Two asynchronous communication primitives, SEND and RECEIVE, have been implemented using Java's remote method invocation mechanism. As an example, we executed a distributed mutual exclusion algorithm on a network of 60 workstations, and measured its performance under various scenarios.

In an external architecture for testing communication protocols, the service provider is an abstraction of the stack of protocols and the public data network interconnecting the lower tester with the implementation under test. In this paper, the effects of a public data network are studied (i.e. the service provider) when testing a protocol implementation's conformity to its specification. The three main characteristics of a service provider are dynamic round trip delay (RTD), message buffering and lossy transmission. It is shown that if proper care is not taken in test architectures and test cases, the dynamic attributes of a service provider may make correct test cases useless. The outline of the paper is as follows. First, the notion of a time server protocol (TSP) in a test architecture to estimate the RTD dynamically in the service provider is introduced. The generalization capability of neural networks is used as the central idea in the dynamic estimation of RTDs from previous actual measurement of delays. Second, the notion of a test architecture's reliability and the properties that a reliable architecture must satisfy are defined. A reliable test architecture is obtained by augmenting a basic test architecture with a TSP and a filter protocol to discard spurious events. Finally, some difficulties in assigning test verdicts are studied and measures are suggested for overcoming them. Thus, an extended service provider is designed by augmenting the actual service provider with the new components. This work proposes an interface to an extended service provider to conceal the side-effects of the bare service provider.

This paper makes two contributions toward computing unique input/output (UIO) sequences in finite-state machines. Our first contribution is to compute all UIO sequences of minimal lengths in a finite-state machine. Our second contribution is to present a generally efficient algorithm to compute a UIO sequence for each state, if it exists. We begin by defining a path vector, vector perturbation, and UIO tree. The perturbation process allows us to construct the complete UIO tree for a machine. Each sequence of input/output from the initial vector of a UIO tree to a singleton vector represents a UIO sequence. Next, we define the idea of an inference rule that allows us to infer UIO sequences of a number of states from the UIO sequence of some state. That is, for a large class of machines, it is possible to compute UIO sequences for all possible states from a small set of initial UIOs. We give a modified depth-first algorithm, called the hybrid approach, that computes a partial UIO tree, called an essential subtree, from which UIO sequences of all possible states can be inferred. Using the concept of projection machines, we show that sometimes it is unnecessary to construct even a partial subtree. We prove that if a machine remains strongly connected after deleting all the converging transitions, then all of the states have UIO sequences. To demonstrate the effectiveness of our approach, we develop a tool to perform experiments using both small and large machines.

This paper contributes toward exception handling and fault-tolerance in multimedia presentation. Our study is based on the well-known four phases in fault-tolerant computing: fault detection, damage assessment, error recovery, and continued service. We define a fault in multimedia synchronization and a simple fault detection mechanism. Using the concept of a partner set of a media stream, we assess the damage caused to media presentation due to a fault. From the point of error recovery and reducing synchronization failures, we introduce the ideas of a k-cycle virtual fault. A k-cycle virtual fault suggests the possibility of a failure in the future after k presentation cycles. Detection of a possible presentation failure in the future gives lead time to take corrective measures to avoid the failure. In order to handle exception conditions during a synchronization failure, we define pinned and sliding semantics of media presentation. These two semantics allow us to define different levels of quality of presentation during a failure. Finally, we present the detailed design of a fault-tolerant presentation architecture and prove its properties. We discuss how the ideas of virtual fault and damage assessment can be used in generating useful information for the underlying data transfer protocol so that synchronization failures can be reduced.

We argue that the programming simplicity and message passing complexity of implementing LOTOS multi-rendezvous largely depends on the underlying communication structure. The programming simplicity and low complexity of our algorithm are due to our viewing the multi-rendezvous problem as a distributed mutual exclusion problem. Using the parallel programming environment of Linda, we present an efficient algorithm to easily implement multi-rendezvous in LOTOS. The orthogonal communication structure in Linda allows us to separate synchronization concerns in multi-rendezvous from concerns in interprocess communication in an implementation. The concept, of Table Space in Linda and the associated in, out, and rd primitives for managing the tuple space lead to an elegant programming style for distributed implementation of multi-rendezvous.