The increasing demand for ultra-reliable and low-latency communication (URLLC) in vehicle road cooperation systems (VRCS) has propelled the development of intelligent and efficient optimization techniques. This paper presents a Quantum Deep Q-Network (QDQN) based approach for minimizing latency in a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) enabled VRCS. STAR-RIS improves signal coverage and energy efficiency by simultaneously serving users in both transmission and reflection modes. However, latency optimization remains a critical challenge due to dynamic environments and computational complexity. The proposed QDQN technique integrates quantum computing principles with deep reinforcement learning (DRL) to accelerate decision making and optimize resource allocation in real time. Using quantum parallelism and entanglement, QDQN reduces convergence time while effectively learning the dynamic state of the communication environment. The simulation results demonstrate that the proposed method achieves a significant latency reduction compared to conventional DRL and classical Q-learning techniques. This study highlights the potential of quantum-enhanced reinforcement learning for future URLLC applications in intelligent vehicular networks.

As new and upcoming technological advancements emerge, the threat of malware will continue to diversify and increase. Malware poses a serious threat to privacy and security of critical data. Some malware do not collect data but use the device's resources for crypto-mining and other activities. Thus, new technology for evading and detecting malware continues to grow and advance. Artificial Intelligence (AI) is one such field that has helped tackle this problem with great precision. Most of the existing solutions use Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and algorithms to make more accurate predictions. However, their solutions are not optimized due to generalized training and higher latency due to large model structures. Inspired by the aforementioned challenge, this paper proposes a distributed learning approach called FedShield to solve the said problem using a Windows Malware dataset. Federated Learning (FL) is one such algorithm that helps to train models distributively, ensuring data security, privacy, scalability, and diversification. Each client model has an ANN that communicates with the global model to update its model via a blockchain layer. The FL model achieves an accuracy of 96 % with 13 clients on the unseen data. Furthermore, the blockchain layer also stores the malicious files in order to make them tamper-proof and secure. The proposed FedShield system is evaluated by comparing it with pre-existing models. The transaction and execution costs in the blockchain for each function are recorded. This approach can help various anti-malware softwares to improve their products.

This paper proposes a new deep learning (DL) model for the detection of infant sleep disorders specific to Confusional Arousals (CA), Leg Restlessness (LR), and Sleep Apnea (SA) in 5G smart homes and e-Healthcare systems integration. The proposed framework, CHILD, consists of four critical layers: specific applications such as Infant Monitoring, Sensor and Data Acquisition, Artificial Intelligence systems and e-Healthcare and Smart Home systems. The smart home part increases the effectiveness of real-time environmental detection, the e-Healthcare system helps to provide convenient communication with doctors. The sleep disorder categorization problem is solved using an enhanced deep learning framework involving LSTM and GRU algorithms; the data are from sensors installed in the smart home., we obtained the 88% accuracy of LSTM model in consideration of the home automation and intelligent e-Healthcare system to enhance infant health and response actions.

Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumented changes which inhibit a fair comparison of algorithms. This work strives to disambiguate these results from the literature, while also leveraging the past versions of Meta-World to provide insights into multi-task and meta-reinforcement learning benchmark design. Through this process we release an open-source version of Meta-World that has full reproducibility of past results, is more technically ergonomic, and gives users more control over the tasks that are included in a task set.

Payment Channel Networks (PCNs) act as a foundational layer in blockchain and deal with the scalability issues. However, a major challenge that continues to hinder the world-wide acceptance of PCNs is the transaction failure rate caused by channel dependency. This depletion occurs due to the dependency of PCNs on network topology. Due to this, effective node placement is crucial for establishing economical channels and strengthening network robustness. On one hand, node attachment is necessary to bring back the PCNs to a balanced state, while on the other hand, the network is prone to getting trapped in a local optimum. To address these challenges, ReLEaRN is proposed as a node placement strategy that deals with the rebalancing problem of PCNs and the local optimum problem using the Soft-Actor critic (SAC) algorithm. We simulated our proposed algorithm on topologies of the Lightning Network and achieved 90 % improvement in execution time when compared with ProfitPilot. The execution time is similar to basic heuristic topologies present in the Lightning Network, but the probability of fees collection is improved to 40 % compared to these topologies.

As Internet of Things (IoT) networks continue to evolve, they face increasing security threats, and methods to achieve the security properties and requirements of these networks from the perspective of data, communication and IoT device security, are still on demand. This paper focuses on Recurrent neural network (RNN)-based methods, which provide intrusion detection systems (IDSs) with the capability of analyzing unseen and complex patterns in exchanges between IoT devices. Two flow-based optimized standalone RNNbased IDSs (called Uni-Hybrid and Bi-Hybrid RNNbased IDSs) are proposed to enhance the security of IoT networks. Through experiments using the IoTID20 dataset, the proposed models yield some marked improvements over a chosen benchmark model in terms of precision, accuracy, recall, and F1-score, highlighting the potential of RRN-based models in addressing intrusion detection in IoT networks.

Inspired by the renaissance of optical computing recently, this poster presents a disruptive outlook on the possibility of seamless integration between optical communications and optical computing infrastructures, paving the way for achieving optical-layer intelligence and consequently boosting the capacity efficiency. This entails a paradigm shift in optical node architecture from the currently used optical-bypass to a novel one, entitled, optical-computing-enabled mode, where in addition to the traditional add-drop and cross-connect functionalities, optical nodes are upgraded to account for optical-computing capabilities between the lightpath entities directly at the optical layer. A preliminary study focusing on the optical aggregation operation is examined and early simulation results indicate a promising spectral saving enabled by the optical-computing-enabled mode compared with the optical-bypass one.

Analog signal transmission has always been a crucial broadcasting technique, particularly in the early days of television. Even with the development of digital technologies, analog signals remain significant, particularly in locations remote from transmission towers. However, analog transmissions are susceptible to damage from noise and interference, which can reduce the signal-to-noise ratio (SNR). This SignalStats explores machine learning-based statistical analysis along with techniques like Adequacy Tweak (AM) and Recurrence Tweak (FM) to optimize interference control in analog stations. A multitude of factors, such as air quality, topography, and transmitter distance, influence signal quality. The project emphasizes data collection and preprocessing approaches in order to enable spatial analysis and visualization to understand station distribution and service provider dominance. Moreover, statistical analysis is used to assess the effectiveness of the signal, channel usage, and ERP. The SignalStats findings provide valuable insights for developing interference control tactics, which in turn improves the efficiency of analog communication networks in the face of rapidly changing technological environments.

Parkinson’s disease (PD) is a neurodegenerative disease which is the second most common neurological disease. Early diagnosis of PD poses significant challenges as in earlier stages of PD, symptoms can’t be clinically recognized. This paper presents a framework called XSH-ParK with integrated deep learning (DL) models and XAI techniques to assist in early PD diagnosis using MRI scans. Pre-trained models VGG16, InceptionV3, ResNet50 and a custom CNN are used to analyze the NTUA dataset, which consists of MRI scans of 78 individuals. Through rigorous evaluation considering accuracy, precision, recall, and F1-score metrics, it is evident that the fine-tuned VGG16 model achieves the highest efficiency with an accuracy rate of 97.56% in the XSH-ParK framework. Additionally, LIME and integrated gradient are the XAI methods used on the top-performing VGG16 model to provide transparent and interpretable diagnostic insights, Enabling healthcare professionals to understand the reasons behind the models’ decisions.

Considering the Activity and Event Network model, also known as AEN, a recent knowledge graph model adept at handling the uncertain and dynamic nature of network activities, this paper proposes an unsupervised anomaly detection system, whose technique is foster around graph clustering within the AEN framework. The goal is to recognize and adapt to normal behaviour across different varying time periods while establishing the baseline behaviours for each cluster. The effectiveness of our proposed system in identifying the anomalies resulted in a false positive rate of 0.85% and a detection rate of 81 %, assessed using the CIC 2018 IDS dataset.

The growth of the opportunistic network (OppNet) in recent years has created a variety of opportunities and concerns. Machine-to-machine (M2M) communications, which are a critical component of OppNet, provide a novel means for connecting and communicating among machine-type communication devices (MTCDs) without the need for human interaction. OppNet data play a significant role in M2M communications and it emphasizes more powerful data storage, computation, processing, as well as the security and stability of data transfer. This paper proposes a joint optimization framework for dueling Deep Q-network (DQN)-based opportunistic data transmission in M2M communication using blockchain technology. The best choice and decision of caching servers, blockchain systems, and computing nodes, can be made in accordance with the dynamic decision-making process by DQN, a decision to increase the system incentives, including high data processing efficiency, lower cost, and improved data interaction security. Simulation results using various parameters demonstrate that the proposed model has more benefits and is more efficient than the existing random, greedy, and Conventional DQN benchmark schemes.

The sixth generation (6G) mobile communication system is expected to utilize millimeter wave and THz frequency bands, in addition to RF bands. The propagation characteristics and ranges of these bands vary vastly while the multi-band users supposedly experience seamless coverage, high throughput and consistent Quality of Service. Heterogeneous base stations (BSs) equipped with these multiple technologies shall be fairly loaded for this. SINR based approaches tend to assign more users to RF channels while starving other bandwidth rich mediums. In this paper, we propose an algorithm to improve the performance of multi-band 6G networks by optimizing the user association to heterogeneous BS to maximize the cumulative data rate while ensuring an acceptable transmission power and fair load balancing among the BSs. The optimization problem is solved using the Lagrangian method. Simulation results show an improved cumulative throughput and fairness.

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine learning-based policy optimization for downlink communication in distributed IRS aided multiple-input single-output (MISO) systems is proposed. Three categories of users are considered, namely, users who can utilize only the direct links, blockage area users who can utilize only the IRS links, and cell edge or poor link quality of users who can utilize both the direct and IRS links. The sum rate maximization problem is formulated to derive the optimal policy (i.e. communication link, IRS selection, power allocation and reflection coefficients) for those users, considering the IRS selection, link quality, power allocation and IRS reflection constraints. The proposed methods to achieve the optimal policy include reinforcement learning-based model with binary decision tree-based user categories, maximum posterior probability-based IRS selection, fractional programming method-based power and IRS coefficient allocation, and value function-based policy optimization. Through simulations, the sum data rate and energy efficiency performances of different categories of users are obtained and discussed.

The Activity and Event Network (AEN) graph is a new framework that allows modeling and detecting intrusions by capturing ongoing security-relevant activity and events occurring at a given organization using a large time-varying graph model. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts. In this paper, we show how known attack methods can be captured generically using attack fingerprints based on the AEN graph. The fingerprints are constructed by identifying attack idiosyncrasies under the form of subgraphs that represent indicators of compromise (IOes), and then encoded using Property Graph Query Language (PGQL) queries. Among the many attack types, three main categories are implemented as a proof of concept in this paper: scanning, denial of service (DoS), and authentication breaches; each category contains its common variations. The experimental evaluation of the fingerprints was carried using a combination of intrusion detection datasets and yielded very encouraging results.

This paper proposes an optimal admission and placement stochastic controller that inserts security and depend-ability in the operational aspects of edge-cloud system under a 5G deployment. The proposed mechanism uses the frame-work of Semi-Markov Decision Making Process (SMDP) and seeks for an optimal policy that efficiently allocates the virtual resources to secure and run the services across the cloudified infrastructure. Driven by a new latency-oriented cost structure, the optimal controller achieves a dependable and secure operation by optimally balancing the service requests between the edge and the cloud system taking into account the service profile, the workload, and the traffic load. A structural analysis of the optimal policy reveals its implementation friendliness while a cloudnomics analysis shows that the optimal cost can be further optimized by fine tuning the parameters of the proposed cost structure.

In this paper, soft clustering on network nodes using Multivariate Gaussian Mixture Models (MGMM) is applied to design a machine learning-based routing protocol for Opportunistic Networks (OppNets). The proposed protocol, called Multivariate Gaussian Mixture-based Prediction routing (MGMP), involves sending messages in concentrated bursts to a group of comparable devices detected using a clustering technique. The network features are utilized for training the MGMM-based clustering model to help identify the relay nodes as the best hop for message transmission. The performance of the proposed MGMP protocol is evaluated using the Haggle Infocom-2006 real dataset and compared against two benchmark protocols KNNR and MLPROPH. The considered performance metrics are average latency, delivery probability and, messages dropped. It has been found that when the TTL is increased, the delivery probability increases and then decreases. Indeed, MGMP outperforms MLPROPH and KNNR in terms of delivery probability by 18.10% and 21.30%, respectively.

Cloud firewalls stand as one of the major building blocks of the cloud security framework protecting the Virtual Private Infrastructure against attacks such as the Distributed Denial of Service (DDoS). In order to fully characterize the cloud firewall operation and gain actionable insights on the design of cloud security, performance models for the cloud firewall become imperative. In this article, we propose a multi-dimensional Continuous-Time Markov Chain model for the cloud firewall that takes into account the burstiness and correlation features of the legitimate and malicious data traffic. By adopting the Markov-Modulated Poisson process (MMPP) and the Interrupted Poisson Process (IPP), we identify the workload conditions under which the cloud firewall might be subject to a loss of availability. Furthermore, by comparing the IPP and Poisson attacks, we numerically verify that the cloud firewall is inherently vulnerable to a burstiness-aware attack which might seriously compromise its operation. Additionally, we characterize the joint harmful impact of burstiness and correlation on the cloud firewall that might lead to performance degradation. Finally, we design an elastic cloud firewall by proposing a MMPP-driven load balancing procedure that provisions virtual firewalls dynamically while fulfilling a Service Level Agreement (SLA) latency specification.

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit their information using low-rate short data packets. Specifically, GB devices are granted unique time-slots for their transmissions. In contrast, GF devices can randomly select time-slots to transmit their messages utilizing the GF non-orthogonal multiple access (NOMA), which has emerged as a promising enabler for massive access and reducing access latency. However, random access (RA) in the GF NOMA can cause collisions and severe interference, leading to system performance degradation. To overcome this issue, we propose a multiple access (MA) protocol based on reinforcement learning for effective RA slots allocation. The proposed learning method aims to guarantee that the GF devices do not cause any collisions to the GB devices and the number of GF devices choosing the same time-slot does not exceed a predetermined threshold to reduce the interference. In addition, based on the results of the RA slots allocation using the proposed method, we derive the approximate closed-form expressions of the average decoding error probability (ADEP) for all devices to characterize the system performance. Our results presented in terms of access efficiency (AE), collision probability (CP), and overall ADEP (OADEP), show that our proposed method can ensure a smooth operation of the GB and GF devices within the same network while significantly minimizing the collision and interference among the device transmissions in the GB-GF HetNet.

This paper proposes a novel routing protocol for Opportunistic networks called Energy Efficient MultiObjectives Optimized Routing (E2MOOR), which uses a multi-objectives weight function for efficient routing. The proposed protocol is energy efficient and predicts the next optimal forwarder based on four objectives, which consists of hop encounter, distance between the source/intermediate and destination, delivery probability, and node’s energy consumption, as context information. The pareto optimal solutions set is extracted through the Naive and Slow algorithm. The nodes in the set are further used for forwarding the data packets to the destination node. The proposed protocol is evaluated considering the double, triple, and quadruple objective functions, when the predefined threshold values are varied. Simulations results show that the proposed E2MOOR scheme outperforms the E-Epidemic, E-PRoPHET, and E-EDR chosen as benchmarks routing protocols.

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To mitigate this issue, several potential multiple access (MA) mechanisms including sparse code MA (SCMA) have been proposed. Besides, the short-packet transmission feature of MTC devices requires the design of new transmission and congestion avoidance techniques as the existing techniques based on the assumption of infinite data-packet length may not be suitable for mMTC networks. Therefore, it is important to find novel solutions to address RACH congestion in mMTC networks while considering SCMA and short-packet communications (SPC). In this paper, we propose an SCMA-based random access (RA) method, in which Q-learning is utilized to dynamically allocate the SCMA codebooks and time-slot groups to MTC devices with the aim of minimizing the RACH congestion in SPC-based mMTC networks. To clarify the benefits of our proposed method, we compare its performance with those of the conventional RA methods with/without Q-learning in terms of RA efficiency and evaluate its convergence. Our simulation results show that the proposed method outperforms the existing methods in overloaded systems, i.e., the number of devices is higher than the number of available RA slots. Moreover, we illustrate the sum rate comparison between SPC and long-packet communications (LPC) when applying the proposed method to achieve more insights on SPC.

This work proposes an optimal security risk management mechanism to holistically minimize the risks of a Denial of Service (DoS) attack and Service Level Agreement (SLA) violations that might unfold at the 5G edge-cloud ecosystem. Using the Semi-Markov Decision Process framework, a cyber risk-aware controller is designed to optimally decide on the admission, placement, and migration of a service taking into consideration a user taxonomy and the service requirements. A new cost structure that balances the targeted security risks as well as the cost and the reward of a secure service provisioning is introduced to pave the way for a safe edge-cloud operation. To proactively restrict the population of untrusted users, we consider security controls in the form of a linear and an exponential cost functions and show that the former represents a more flexible and profitable pathway for a Mobile Network Operator to operate at the expense of an inflated security risk while the latter leads to the opposite outcome. Results show that the baseline mechanism might violate the SLA and expose the edge and the cloud to a DoS attack in levels that are 102, 1012, and 1014times higher than those of the proposed controller.

This paper proposes a novel routing protocol for opportunistic networks called Fuzzy logic-based Q-Learning Routing Protocol (FQLRP), which uses fuzzy based Qlearning for efficient routing. The proposed protocol predicts the next optimal forwarder of a message based on a reward mechanism that considers the node's energy, movement, and buffer space as parameters. Throughout the routing process, the residual energy of each node and the energy distribution of a group of nodes, are both considered in determining a reward function, which in turn helps in deciding the most suitable forwarders of the message towards its destination. Simulation results show that the proposed FQLRP scheme outperforms the Q-Learning based routing and the Epidemic routing protocols, chosen as benchmarks, in terms of delivery rate, average delay and overhead ratio.

Opportunistic Networks are composed of wireless nodes opportunistically communicating with each other following the store, carry and forward mechanism. These networks are designed to operate in an environment characterized by high delay, intermittent connectivity and non-guarantee of the end-to-end path between the sender and the destination. The messages are transmitted on the basis of best-effort procedure. If the nodes are not able to forward the message for reasons like missing connectivity, insufficient buffer space or low-confidence among nodes, the messages are temporarily buffered according to the waiting-list policy and it is resumed when the connection is established again. The nodes drop the message on the basis of delete policy in a congested network environment. While there are multiple policies for effective buffer utilization in Opportunistic Networks such as FIFO, LIFO, and Random, none allow message transmission on the basis of message- type. In this paper, a Priority based Buffer Management Technique (PBMT) has been introduced that considers the priority of a message to address the aforementioned problems. This policy allows solving the underlying problem of transmitting messages in a random fashion, by transmitting them in a systematic and orderly method. The proposed PBMT shows considerable difference in routing processes. Simulation results that are provided, confirm that the proposed PBMT is more secure and efficient than traditional buffer management policies for opportunistic networks by using the Haggle INFOCOM 2006 real mobility data trace.

The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

Internet of things (IoT) is a revolution of the internet where a group of computing devices, sensors, machines or people, having unique identifiers and the ability to transfer data over the network without human intervention, are interconnected. Opportunistic networks (OppNets) are a type of disruption‐tolerant networks, where network topology is not fixed and the devices are connected intermittently. Opportunistic IOT (OppIoT) is a blend of OppNets and IoT networks, where the data are shared among IoT devices and human communities exploiting the opportunistic contact nature of humans. The data is usually transmitted in a broadcast manner, exposing it to all the members of the network. Thus, securing the data transmitted is of utmost importance in OppIoT. This article proposes a trust‐based schemE (called T_CAFE) for securing the network against several attacks like sybil, bad mouthing, good mouthing, black hole and packet fabrication attacks. Using the opportunistic network environment simulator for performing simulations, it is found that the proposed T_CAFE protocol enhances the network security and outperforms routing protocols such as SHBPR, RSASec and ATDTN in terms of legitimate packet delivery, higher probability of message delivery, lower count of dropped messages and lower value of latency in packet delivery.

This paper proposes a new online signature verification system based on fuzzy modelling which involves the modification of the fuzzy membership function with the help of structural parameters. By using these structural parameters, the signature of a given user can be easily verified. The proposed approach relies on the extraction of features from the sample data collected from users under different situations and time intervals. Two distinct methodologies have been suggested to obtain the structural parameters: the first method is based on the Shannon entropy functions coupled with an objective function defined in terms of error while the second is based on the interaction among the input fuzzy sets which is computed using s-norms. Both genuine and forged signatures were tested using the proposed techniques with encouraging results.

The next generation of mobile telephony aims to attain the increasing demands of users in terms of flexibility, bandwidth, spectral efficiency, energy efficiency, low latency, and quality of service. In this context, adaptations to the transmission system are needed in order to be able to cope with the exponential high-speed data increase with reliability, but also to cope with the expected applications, such as the Internet of Things (IoT) and tactile Internet. Researches have studied the adaptations that should be performed and one of the modifications is the physical layer used to transmit the signal. This paper aims to analyze and optimize the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) and General Frequency Division Multiplexing (GFDM) which is a technique among the different types of modulation studied for 5G, operating in an Additive White Gaussian (AWGN) channel. This work also presents the performance of the possible received power levels according to the receiver and Ultra High Frequency (UHF) channels in the case of digital television. Fourier Transform is used as the mathematical model for performance evaluation and analyzes. The obtained results show that GFDM performs better in terms of interferences when Zero Forcing Receiver (ZFR) equalization is considered, which plays a key role in digital transmission.

Opportunistic networks (OppNets) is a subclass of delay-tolerant networks characterized by unstable topology, intermittent connectivity, and no guarantee of the existence of an end-to-end path between the source and destination nodes. In such networks, data forwarding from a source node to a destination node is a challenge. In this paper, an energy-aware routing protocol for OppNets (so-called Energy-efficient Location Prediction-based Forwarding for Routing using Markov Chain (ELPFR-MC)) is proposed, in which the next best hop selection of a message relies on the use of the node's residual energy and its location based on delivery probability. Simulation results show that ELPFR-MC is superior to E-Prophet, E-PRoWait, E-EDR and the Distance and Encounter based Energy-efficient Protocol for Opportunistic Networks (DEEP), where E-Prophet, E-PRoWait, E-EDR are respectively the energy-aware implemented versions of the Prophet, PRoWait, and Encounter and Distance-based Routing (EDR) protocols. The proposed ELPFR-MC outperforms DEEP in terms of node's residual energy by 6.34%, number of dead nodes by 6.58%, message delivery probability by 16.83% and average latency by 8% respectively when number of nodes are varied.

Handoff in a cognitive radio networks (CRNs) is a situation that arises whenever a secondary user (SU) has to switch from its current channel to a new target channel in case the primary user (PU) reclaims the current channel or the channel conditions get worst. Before starting the SU transmission, a proactive decision to select the prospective vacant target channel after the PU interruption to resume the unfinished transmission can save substantial sensing time. In addition, the sequence of backup target channels can help reducing the service time of a SU considerably. This paper proposes a proactive decision based handoff scheme for CRNs, in which a non- iterative greedy approach is implemented to proactively determine the optimal target channel sequence without requiring the usual brute force strategy. Simulation results show that the proposed approach outperforms the reactive approach as well as a chosen benchmark scheme in terms of service time and number of handoffs. A comparative performance is also obtained in terms of throughput achieved by the SU under varying PU traffic.

The design of routing protocols for opportunistic networks (OppNets) generally assume that some cooperation prevail between the nodes. But, in the presence of non-collaborative attacks such as supernova and hypernova, the routing operations become a challenge. This paper proposes a defense mechanism against the misbehavior of supernova and hypernova nodes in an OppNet running Epidemic and ProPHet as underlying routing protocols, respectively. Simulation results are provided, under various opportunistic routing attack scenarios, showing that our proposed mechanism helps defending against such attacks, while yielding an increased average performance in terms of delivery ratio and delay in message delivery by 21% and 19% for Epidemic and ProPHet, respectively.

Opportunistic Networks (OppNets) are a kind of challenged ad hoc networks where frequent topology changes, intermittent connectivity, and no guarantee of end-to-end path prevail. In this context, designing energy-efficient routing protocols for OppNets is quite challenging since most of the node's energy is consumed during node discovery and message transmission. This paper proposes the energy-efficient versions of three existing routing protocols for OppNets, namely, the probability routing protocol using history of encounters and transitivity (Prophet), the Probability-based controlled flooding in opportunistic networks (PRoWait), and the Encounter and Distance based Routing Protocol for Opportunistic Networks (EDR), where the selection of the next best forwarder for a message relies on the energy (or battery power) of the nodes. These energy-aware protocols are referred to as E-Prophet, E-PRoWait and E-EDR schemes. Simulations results show that E-Prophet, E-PRoWait and E-EDR yield a better energy consumption compared to Prophet, ProWait and EDR.

Handoff in cognitive radio networks (CRNs) is a situation that arises whenever a secondary user (SU) has to switch from its current channel to a new target channel in case the primary user (PU) reclaims the current channel. Often when a SU switches to a target channel, it finds that it has to share the target channel with the coexistent users. These coexistent users can either be the interrupted or non-interrupted SUs who also wish to share the same channel. Long waiting in a queue or simultaneous access to the channel may decrease the SU's and network's throughput considerably. The SUs may even behave selfishly to maximize their own throughput. This paper analyzes the interactions and behavior of the SUs during the target channel sharing through non-cooperative, mixed strategic, and cooperative games. The benefits of the SUs and the overall network is analyzed by finding the Nash equilibrium and the Nash bargaining solution (NBS) for the non-cooperative, mixed strategy, and cooperative game respectively.

The e-Health core concept includes Web usage in an integrated way with tools and services for healthcare. This definition improves access, efficiency, and clinical care quality process that are necessary for a service delivery improvement. Decision support systems (DSSs) belong to a plethora of e-Health concept dimensions. For these systems construction, it is important to find a reliable intelligent mechanism capable to identify diseases that can worsen the patient's clinical condition. Thus, this paper proposes the use of tree-based data mining (DM) techniques for the hypertensive disorders prediction in the risk gestation. It presents the modeling, performance evaluation, and comparison between the tree based classifiers ID3 and NBTree. The 5-fold cross-validation method realizes the performance comparison. Results show that the NBTree classifier obtained better performance, presenting F-measure 0.609, ROC area 0.753, and Kappa statistic 0.4658. This classifier can be a key to a smart system development capable to predict risk events in pregnancy. Therefore, DSSs are a leading solution for the reduction of both mother and fetal mortality.

As the multitude and complexity of the cloud market increases, the evaluation and selection of cloud services becomes a burdensome task for the users. With the extraordinary rise of available services from various Cloud Service Providers (CSPs), the role of cloud brokers has become more and more important. This paper proposes an optimal cloud broker model to address the challenge of optimally allocating multiple cloud system resources to multiple mobile user's requests with different requirements. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria. The overall system cost takes into consideration the cost of occupying computing resources, the communication costs, the request traffic, as well as various security risk degrees and resource requirements from the various mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is calculated by means of the Value Iteration Algorithm. Some analysis are conducted and numerical results are presented, demonstrating the feasibility of the proposed cloud broker design.

In an Opportunistic Network (Oppnet), the transmission of messages between mobile devices is achieved in a store-carry-and-forward fashion since nodes store the incoming messages in their buffer and wait until a suitable next hop node is encountered that can carry the message closer to the destination. In such environment, due to the delay-tolerant nature of the network, designing a routing protocol is a challenge. This paper proposes a novel routing protocol called Encounter and Distance based Routing (EDR), which utilizes the so-called forward parameter to determine the next hop selection. This parameter is calculated by taking into account the number of encounters and the distance of each node in the network with respect to a particular destination. Simulation results are provided, showing the superiority of EDR over the History based Prediction for Routing (HBPR) protocol and the ProWait protocol, chosen as benchmark schemes, in terms of hop count, messages dropped, and average latency.

In Opportunistic networks (OppNets), due to high mobility, short radio range, intermittent links, unstable topology, sparse connectivity, to name a few, routing is a very challenging task since it relies on cooperation between the nodes. This paper focuses on using the concept of centrality to alleviate this task. Unlike other nodes in the network, central nodes are those that are more likely to act as communication hubs to facilitate the message forwarding and thereby routing. In this paper, a recently proposed History-Based Prediction Routing protocol (HBPR) for OppNets is re-designed using this concept, yielding the so-called centrality-based HBPR (CHBPR) routing protocol. The proposed CHBPR scheme is evaluated by simulations using the Opportunistic NEtwork (ONE) simulator, showing superior performance compared to HBPR without centrality and Epidemic protocol with centrality, in terms of number of messages delivered at destination and overhead ratio, under varying number of nodes and Time-to-Live.

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm. For evaluation we considered respiratory rate and heart rate metrics. The simulation results show that the k-means clustering algorithm has a good performance.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers, because of their potential use in several fields such as collaborative computing and communications. In this paper, we present the implementation of a testbed for WMNs using Raspbian OS. We analyze the performance of Optimized Link State Routing (OLSR) protocol in an indoor environment considering Transmission Control Protocol (TCP) and Line-of-Sight (LoS) scenario. For evaluation we considered throughput, hop count, delay and jitter metrics. The experimental results show that the nodes in the testbed were communicating smoothly.

With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Particle Swam Optimization for solving node placement problem in wireless mesh networks, called WMN-PSO. In this paper, we use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. Then, we analyze effects of weight-coefficients for SGC and NCMC. From the simulation results, we found that the best values of the weight-coefficients for SGC and NCMC are 0.7 and 0.3, respectively.

Cognitive radio is an optimistic technology to implement the concept of dynamic spectrum access and provide a flexible way to share the spectrum among the primary and secondary users. In this paper, a game theoretic-based model is presented using the concept of Nash Equilibrium for spectrum sharing. In this model, interference and number of radios on each link are considered as parameters for designing the game. An algorithm for channel allocation among the users is also presented. From the simulation analysis, it is observed that the system performs satisfactorily in terms of network utilization. Also, the Taguichi method is applied and an analysis of variance (ANOVA) is performed, proving that the design parameters taken into consideration in our proposed method are impactful.

With the ever increasing data and computational demands from mobile users, heterogenous wireless networks (HetNets) and mobile cloud computing (MCC) have been advocated as a promising solution to meet these demands. Insufficient bandwidth is one of the most important challenges being faced by a successful implementation of the MCC technology due to heavy data traffic. The MCC implementation on HetNet increases the bandwidth available to each base station (BS) by frequency reuse. In this paper, a novel data storage method for big data files is proposed, along with a data correction technique to deal with the issue of failure of a data chunk retrieval. The proposed algorithm exploits the multiple paths that are available between a user and the cloud storage system in a MCC-HetNet environment. Since the bottleneck in the MCC is the wireless link between the user equipment (UE) and the BS, we have implemented the algorithm on the wireless links between the UE and the BS. The simulated results show that the proposed method outperforms the conventional data storage method between the mobile device and the cloud system.

Cognitive radio networks play a vital role in solving the problem of underutilization of spectrum by identifying unused licensed radio spectrum. They solve the problem by distributing this unused spectrum among unlicensed users in an intelligent fashion without interfering with existing users. In this paper, using the conflict graph and graph colouring concepts, a Graph Colouring based Dynamic Channel Allocation (GC-DCA) algorithm is proposed that minimizes the network interference when the primary users (PUs) and secondary users (SUs) share the channel simultaneously. The performance of the GC-DCA algorithm is evaluated using the OMNeT++ network simulator under different topologies, in terms of channel utilization, end-to-end delay, packet delivery ratio, and throughput. Numerical results show that the proposed technique yields 40.40% increase in channel utilization when the PU and SU share the channel simultaneously.

International audience

A mobile ad hoc network is a collection of mobiles, autonomous nodes that communicate in a cooperative manner over a wireless channel without any fixed infrastructure, nor built-in security. As such, this type of network is vulnerable to different types of attacks such as blackhole and wormhole attacks. A blackhole attack is a type of attack where the malicious node so-called blackhole node can attract all the data packets by using a forged route reply packet to falsely claim that it has a shortest route to the destination, thereby dropping all the data packets that it receives. In this paper, an improved version of a dynamic source routing DSR protocol so-called detecting blackhole attack based on DSR DBA-DSR is proposed to combat against blackhole attacks in mobile ad hoc networks. Unlike other solutions, which adopt a reactive approach in which blackhole nodes are identified only after the attack has been carried out on the network, our DBA-DSR scheme detects and isolates the blackhole nodes prior to the actual routing process. This is achieved by using fake route request packets. Simulation results are provided, demonstrating the superiority of DBA-DSR over DSR in terms of network throughput, packet delivery ratio, and routing overhead, chosen as performance metrics, when blackhole nodes are present in the network. Copyright © 2013 John Wiley & Sons, Ltd.

In mobile ad hoc networks (MANETs), nodes are mobile and have limited energy resource that can quickly deplete due to multi-hop routing activities, which may gradually lead to an un-operational network. In the past decade, the hunt for a reliable and energy-efficient MANETs routing protocol has been extensively researched. This paper proposes a novel Ant Net-based routing scheme for MANETs (so-called MAnt Net), and an its enhanced energy-aware version (so-called E-MAnt Net), for which the routing decisions are facilitated based on the nodes' residual energy. These protocols were evaluated through simulations using NS2, showing that E-MAnt Net outperforms both MAnt Net and EAODV, in terms of network residual energy, network lifetime, number of established connections, and the number of dead nodes in the network, where E-AODV is an energy-aware version of AODV.

A major problem faced by machine type communication (MTC) devices in machine to machine (M2M) communication is the congestion and traffic overloading when incorporating into LTE Advanced networks. In this paper, we present an approach to tackle this problem by providing an efficient way for multiple access in the network and minimizing network overload. We consider the random access network (RAN) between the LTE base stations and MTC devices in the cell. We propose an unsupervised learning algorithm, based on Q-learning, as a means of base station selection scheme where MTC devices continuously adapt to changing network traffic and decide which base station is to be selected on the basis of QoS parameters. Simulation results demonstrate that the proposed algorithm helps MTC devices achieve better performance and, therefore, enhances the M2M communication performance.

Mobile ad hoc networks (MANETs) are infrastructureless. As such, they are subject to various types of security attacks if malicious nodes are present in the network. One of such attacks is the wormhole attack. In an earlier work, a scheme (called Cell-based Open Tunnel Avoidance (COTA)) was proposed to address this problem, which consisted in a mechanism for detecting and classifying the wormhole attacks in the network. In this paper, the COTA mechanism is implemented on the location aided routing protocol (LAR1), leading to the so-called COTA-LAR1 scheme. Simulation results are provided, showing that the COTA-LAR1 scheme is an improved secured routing scheme against wormhole attacks in MANETs, in terms of packet delivery ratio, throughput, and end-to-end delay, chosen as performance metrics.

Abstract A utility‐based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game‐theoretic heuristic load‐balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy‐efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load‐balanced trees, resulting in over 30%longer network lifetime compared with the cumulative algorithm proposed in the literature. Copyright © 2014 John Wiley & Sons, Ltd.

Continuous Authentication (CA) consists of monitoring and checking repeatedly and unobtrusively user behavior during a computing session in order to discriminate between legitimate and impostor behaviors. Stylometry analysis, which consists of checking whether a target document was written or not by a specific individual, could potentially be used for CA. In this work, we adapt existing stylometric features and develop a new authorship verification model applicable for continuous authentication. We use existing lexical, syntactic, and application specific features, and propose new features based on n-gram analysis. We start initially with a large features set, and identify a reduced number of user-specific features by computing the information gain. In addition, our approach includes a strategy to circumvent issues regarding unbalanced dataset which is an inherent problem in stylometry analysis. We use Support Vector Machine (SVM) for classification. Experimental evaluation based on the Enron email dataset involving 76 authors yields very promising results consisting of an Equal Error Rate (EER) of 12.42% for message blocks of 500 characters.

Workload and resource management are two essential functions provided at the service level of the distributed systems infrastructure. To improve the global throughput of these software environments, workloads have to be evenly scheduled among the available resources. To realize this goal, several load balancing strategies and algorithms have been proposed. Most o f t h e s e strategies were developed assuming homogeneous set of sites linked with homogeneous and fast networks. However, for computational grids, we must address some new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a decentralized cluster-based algorithm which achieves dynamic load balancing in the cloud architecture. The proposed algorithm presents the following main features: (i) it supports heterogeneity, (ii) scalability, (iii) low network congestion and (iv) absence of any bottleneck node due to its decentralized nature. Simulation results using CloudSim show the performance analysis of the algorithm for patronizing our claims about the load balancing achieved in the system.

ABSTRACT In mobile ad hoc networks (MANETs), nodes are mobile in nature, but at the same time, they are assumed to rely on each other to relay their traffic even in case the wireless transmission medium is out of range. This requirement poses a serious challenge when malicious nodes are present in the MANET and may contribute to the routing operations, either by tampering the data packets or dropping them. This paper addresses this particular type of wormhole attacks, by introducing an enhancement (the so‐called E‐HSAM) to a recently proposed ad hoc on‐demand distance vector‐based protocol for preventing against such attacks in MANETs (the so‐called highly secured approach against attacks on MANETs (HSAM)). Our contributions are twofold: (i) a simulation study of the HSAM protocol is provided for the first time, and (ii) the Advanced Encryption Standard (AES) is introduced in the route selection phase of E‐HSAM (yielding our so‐called E‐HSAM‐AES scheme) to strengthen the integrity of the data while securing the potential routes chosen for data transfer from source to destination nodes. Simulation results are presented, showing the superiority of E‐HSAM‐AES over E‐HSAM and HSAM in terms of packet delivery ratio and broken link detected during data transmission, chosen as performance metrics. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In wireless sensor networks, achieving load balancing in an energy‐efficient manner to improve the network lifetime as much as possible is still a challenging problem because in such networks, the only energy resource for sensor nodes is their battery supplies. This paper proposes a game theoretical‐based solution in the form of a distributed algorithm for constructing load‐balanced routing trees in wireless sensor networks. In our algorithm, load balancing is realized by adjusting the number of children among parents as much as possible, where child adjustment is considered as a game between the parents and child nodes; parents are considered as cooperative players, and children are considered as selfish players. The gained utility by each node is determined by means of some utility functions defined per role, which themselves determine the behavior of nodes in each role. When the game is over, each node gains the maximum benefit on the basis of its utility function, and the balanced tree is constructed. The proposed method provides additional benefits when in‐network aggregation is applied. Analytical and simulation results are provided, demonstrating that our proposed algorithm outperform two recently proposed benchmarking algorithms [1, 2], in terms of time complexity and communication overhead required for constructing the load‐balanced routing trees. Copyright © 2012 John Wiley & Sons, Ltd.

Recent advances in body area network technologies such as radio frequency identification and ham radio, to name a few, have introduced a huge gap between the use of current wireless sensor network technologies and specific needs of some important wireless sensor network applications such as medical care, disaster relief, or emergency preparedness and response. In these types of applications, the mobility of nodes can occur, leading to the challenge of mobility handling. In this paper, we address this challenge by prioritizing transmissions of mobile nodes over static nodes. This is achieved by using shorter contention windows in reservation slots for mobile nodes the so-called backoff technique combined with a novel hybrid medium access control MAC protocol the so-called versatile MAC. The proposed protocol advocates channel reuse for bandwidth efficiency and management purpose. Through extensive simulations, our protocol is compared with other MAC alternatives such as time division multiple access and IEEE 802.11 with request to send/clear to send exchange, chosen as benchmarks. The performance metrics used are bandwidth utilization, fairness of medium access, and energy consumption. The superiority of versatile MAC against the studied benchmark protocols is established with respect to these metrics. Copyright © 2012 John Wiley & Sons, Ltd.

With the advent of cloud computing as a new paradigm and technology, and the increased tendency of decision makers to envision a staged migration to cloud services, most enterprises are choosing to outsource their data to cloud storage providers, for better management of their IT resources, in terms of security, control, space and storage costs. In this context, assuming that the cloud service provider may not be trustworthy (i.e. is honest but curious), ensuring data privacy in all operations performed on enterprise data while these data reside in the Cloud is still a challenge. This paper proposes a novel twolevel data deduplication framework that can be used in cloud storage by enterprises. At the enterprise level, the enterprise performs cross-user data deduplication and outsources its data to the Cloud. At the cloud storage provider level, cross enterprise data deduplication is performed by the cloud service provider to further remove duplicates, resulting in cost and space savings. We argue that our framework will allow the enterprise to facilitate operations such as searching over encrypted data, sharing data within the enterprise, and downloading data from the Cloud directly, in a secure and efficient manner without the need to trust the cloud service provider.

In Opportunistic Networks (OppNets), the existence of an end-to-end connected path between the sender and the receiver is not possible. Thus routing in this type of networks is different from the traditional Mobile Adhoc Networks (MANETs). MANETs assume the existence of a fixed route between the sender and the receiver before the start of the communication and till its completion. Routes are constructed dynamically as the source node or an intermediate node can choose any node as next hop from a group of neighbors assuming that it will take the message closer to the destination node or deliver to the destination itself. In this paper, we proposed a novel History Based Prediction Routing (HBPR) protocol for infrastructure-less OppNets which utilizes the behavioral information of the nodes to find the best next node for routing. The proposed protocol was compared with the Epidemic routing protocol. Through simulations it was found that the HBPR performs better in terms of number of messages delivered and the overhead ratio than the Epidemic protocol.

As the world's economic activities are expanding, the energy comes to the fore to the question of the sustainable growth in all technological areas, including wireless mobile networking. Energy-aware routing schemes for wireless networks have spurred a great deal of recent research towards achieving this goal. Recently, an energy-aware routing protocol for MANETs was proposed by us, in which the energy load among nodes is balanced so that a minimum energy level is maintained and the resulting network lifetime is increased. In this paper, an Ant Colony Optimization (ACO) inspired approach to EEAODR (so-called ACO-EEAODR) is proposed. To the best of our knowledge, no attempts have been made so far in this direction. The obtained simulation results show that the ACO-EEAODR outperforms the EEAODR scheme in terms of energy consumed and network lifetime performance metrics.

Opportunistic networks (Oppnets) are a kind of wireless networks that provide the opportunity to have social interaction and obtain data that can be used for message passing decision. The increase observed in the number of people with PDAs and other handset devices equipped with wireless technologies makes the forwarding paradigm and Oppnets scenarios more interesting and challenging. The main challenge in Oppnets is to take efficient routing decisions on securing the delivery of messages to the destination. Cooperation and trust between nodes in the network saves them from malicious attacks. The trust of a node is a basic value that symbolizes the magnitude of its social responsibility in the network, which include helping groups of nodes in message delivery, saving these nodes from malicious attacks, just to name a few. This paper focuses on blackhole attack against the PRoPHET routing protocol for Oppnets. A Trust-based Security Protocol (TSP) is proposed to secure Oppnets against blackhole attacks. Simulation results are provided to support the effectiveness of our proposed TSP approach, in the sense that considerable control is observed in the number of dropped packets, number of messages captured bythe malicious nodes (so-called malicious count) and overhead ratio.

Wireless mesh networks (WMNs) have become immensely popular these days. In order to satisfy user service requirements, multimedia applications need quality of service (QoS) support. Since the mesh routers are usually stationary in WMNs, a better performance is expected in WMNs as compared to ad hoc networks. But sustaining QoS in wireless mesh networks still remains a challenging task. A fundamental management function in WMNs for backing up multimedia applications is to have admission control. In this paper, we propose a protocol called Rate Adaptive Routing on Cliques Admission Control (RA-RCAC) that provides rate adaptive admission control such that the network layer provides feedback in case of network congestion at the application layer. By simulations, RA-RCAC is compared against MARIA [1] and RCAC [2], showing its superiority in terms of throughput, end-to-end delay, packet delivery ratio and loss ratio, chosen as performance metrics.

In the recent years, Peer-to-Peer (P2P) sharing network has become very popular in the Internet. However, most P2P protocols are designed for traditional wired networks. When deployed in wireless network environment, many challenges are encountered. For instance, the nodes in an unstable wireless network tend to leave or rejoin the P2P network easily. In this case, the routing information in every node must become overdue, which may lead to lookup failures when the nodes retrieve these overdue routing information. In this paper, we propose a modified Chord protocol called MobileRobust-Chord (MR-Chord). MR-Chord is designed with the aim of keeping the Finger Table fresh. To achieve this goal, we have modified the Distributed Hash Table (DHT)-based protocol a Chord Protocol in such a way that the Finger Table is kept updated to provide the necessary lookup services in the P2P network. Simulations studies show that our proposed MR-Chord protocol outperforms the original Chord protocol in the following aspects: (1) increase in the lookup success rate and overlay consistency, (2) reduction of the lookup delay time.

Securing the routing of message in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes an enhanced trust-based multipath Dynamic Source Routing (DSR) protocol (so-called ETB-MDSR) to securely transmit messages in MANETs. Our method consists in a combination of soft-encryption, novel trust management strategy, and multipath DSR routing. Simulation results are presented to validate our proposal, showing that our ETB-MDSR scheme outperforms a recently proposed Trust-Based Multipath DSR message scheme (TB-MDSR), in terms of route selection time.

Cloud computing has empowered the individual user by providing seemingly unlimited storage space and availability and accessibility of data anytime and anywhere. Cloud service providers are able to maximize data storage space by incorporating data deduplication into cloud storage. Although data deduplication removes data redundancy and data replication, it also introduces major data privacy and security issues for the user. In this paper, a new privacy-preserving framework that addresses this issue is proposed. Our framework uses an efficient deduplication algorithm to divide a given file into smaller units. These units are then encrypted by the user using the combination of a secure hash function and a block encryption algorithm. An index tree of hash values of these units is also generated and encrypted using an asymmetric search encryption scheme by the user. This index tree will enable the cloud service provider to search through the index and return the requested units. We will show that our proposed framework will allow cloud service and storage providers to employ data deduplication techniques without giving them access to either the users' plaintexts or the users' decryption keys.

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

In recent years, multimedia network services have moved from a single service to rich services which integrate multiservice capabilities integration. If all service requests require the user to send the request by himself, this will result to a huge control function load and complex service collaboration. In order to address service interaction and reuse the service capability, 3GPP proposes a Service Capability Interaction Manager, which can provide service capabilities invocation and service interaction management between Application Servers (ASs) and Serving-Call Session Control Function (S-CSCF). However, its architecture may cause joint and cooperation problems between the different service providers. In this paper, we propose a scalable service capability interaction framework called ServiceChord that can process multiple service capabilities with different ASs and reduce the call set-up delay while communicating with the S-CSCF. The Chord DHT technique is used to improve the framework, leading to a reduction of message redundancy on the S-CSCF while achieving an efficient service capability interaction, and providing scalability for IMS services and ASs.

Mobile Ad Hoc Networks (MANETs) offer a dynamic environment in which data exchange can occur without the need of a centralized server or human authority, providing that nodes cooperate among each other for routing. In such an environment, the protection of data en route to its destination is still a challenging issue in the presence of malevolent nodes. This paper proposes a message security approach in MANETs that uses a trust-based multipath AOMDV routing combined with soft-encryption, yielding our so-called T-AOMDV scheme. Simulation results using ns2 demonstrate that our scheme is much more secured than traditional multipath routing algorithms and a recently proposed message security scheme for MANETs (our so-called Trust-based Multipath Routing scheme (T-DSR)), chosen as benchmark. The performance criteria used are route selection time and trust compromise.

Securing the transfer of data in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes a method for providing message security in MANETs when nodes cooperate in routing. Our approach combines a trust-based multipath routing scheme and a real-time recurrent neural network-based (RRNN) cipher (yielding our so-called TR-RRNN scheme) to deal with the issues underlying message confidentiality, integrity, and access control. Simulation experiments using QualNet were conducted, showing that the proposed scheme is much more secured compared to the traditional multi-path routing algorithms and a recently proposed message security scheme for MANETs (our so- called Original Trust-based Multi-path Routing scheme (OTMR)). The route selection time and trust compromise are used as the performance criteria.

In this paper, the problem of mobility handling in wireless sensor network (WSN) is studied with a simple priority backoff technique. To incorporate this technique for stationary and mobile sensor nodes, a novel hybrid MAC protocol called VMAC is designed with a fixed frame length. VMAC combines the advantages of schedule-based MAC for energy savings and contention-based MAC for short transmission delays. To exploit the bandwidth in the network, channel reuse is encouraged and is readily integrated into the protocol. Simulation results using ns2 demonstrate that VMAC with certain frame lengths are suited for selected topologies, but the frame length of one provides sufficient performance. It is also shown that the energy consumption of VMAC is roughly one-third lower compared to pure schedule-based protocol while the average delay is about two-fold less than that of contention-based protocol in one-hop communication scenarios with frame length of one; meaning that VMAC performs very well in short-range communication. The backoff technique is also shown to be fair when nodes contend for medium access and it is even resourceful in speeding up hardware address resolution and routing.

Wireless sensor networks suffer from the problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address both node-level and link-level congestion and propose a new routing protocol namely ant based routing with congestion control (ARCC), for wireless sensor networks, which takes into account the congestion of the network at a given instance and proposes to reduce it and then finds the optimum paths. Also, a comparison of simulation with a few existing works highlights the edge that ARCC has over its contemporaries in terms of various network quality parameters.

A P2P file sharing system implementation on mobile ad-hoc networks is quite tricky to implement as compared to that on a wired network. With the use of Swarm Intelligence, the P2P file Sharing methodology not only has an optimized search process involving a more selective node tracing but also provides a far more time efficient and robust sharing mechanism. A P2P File sharing system implementation poses (a) percentage network area scanned and (b) selective file retrieval from a set of file bearing nodes as the biggest challenge. In this paper, we propose to use another Swarm Intelligence Technique Bees Algorithm - P2PBA (Peer to Peer file sharing - Bees Algorithm) to tackle these issues. Based on the lines of food search behavior of Honey Bees, it optimizes the search process by selectively going to more promising honey sources and scan through a sizeable area. Following a description of the algorithm, the paper gives simulation results for the network against specified parameters that our algorithm proposes to make file sharing technique more efficient.

Key management involves two aspects: key distribution and key revocation. This paper presents the geographic server distributed model for key revocation which concerns about the security and performance of the system. The concept presented in this paper is more reliable, faster and scalable than the existing revocation techniques used in public key infrastructure (PKI) framework in various countries, as it optimises key authentication in a network. It proposes auto-seeking of a geographically distributed certifying authority's key revocation server, which holds the revocation lists by the client, based on the best service availability. The network is divided itself into the strongest availability zones (SAZ), which automatically allows the new receiver to update the address of the authentication server and replace the old address with the new address of the SAZ, in case it moves to another location in the zone, or in case the server becomes unavailable in the same zone. Our scheme eases out the revocation mechanism and enables key revocation in the legacy systems.

This paper addresses the survivability in existing ATM mesh networks with the goal to (1) compare the network survivability for link and path restorations, (2) to determine the effect of spare capacity distribution schemes on the restoration ratio, and (3) to determine the effect of the choice of candidate paths per node pair on the restoration ratio. It is observed that our results can contribute to enhance the design decisions when dealing with survivable ATM mesh-based network designs, for the predefined restoration objective.

In a pervasive wireless network, malicious nodes can initiate attacks to well-behaved nodes. This paper argues that our recently proposed probabilistic trust management scheme for pervasive computing can be used in pervasive wireless networks to provide protection against few typical attacks usually targeted at such systems. Simulation experiments are provided to assess the achievement of the stated goal. The performance metric used is the average packet loss ratio, representing the ratio of packets lost to the total packets generated in a certain time period.

Presents the introductory welcome message from the conference proceedings.

Owing to the growing demand for low-cost ‘networkable’ sensors in conjunction with recent developments of micro-electro mechanical system (MEMS) and radio frequency (RF) technology, new sensors come with advanced functionalities for processing and communication. Since these nodes are normally very small and powered with irreplaceable batteries, efficient use of energy is paramount and one of the most challenging tasks in designing wireless sensor networks (WSN). A new energy-aware WSN routing protocol, reliable and energy efficient protocol (REEP), which is proposed, makes sensor nodes establish more reliable and energy-efficient paths for data transmission. The performance of REEP has been evaluated under different scenarios, and has been found to be superior to the popular data-centric routing protocol, directed-diffusion (DD) (discussed by Intanagonwiwat et al. in ‘Directed diffusion for wireless sensor networking’ IEEE/ACM Trans. Netw., 2003, 11(1), pp. 2–16), used as the benchmark.

In this paper, a neural network-based symmetric cipher design methodology is proposed to provide high performance data encryption. The proposed approach is a novel attempt to apply the parallel processing capability of neural networks for cryptography purposes. By incorporating neural networks approach, the proposed cipher releases the constraint on the length of the secret key. The proposed cipher is robust in resisting different cryptanalysis attacks and provides efficient data integrity and authentication services. The design of the symmetric cipher is presented and its security is analyzed. Simulation results are presented to validate the effectiveness of the proposed cipher design.

Let V=(n, k:) be an irreducible cyclic code over F/sub q//sup m/, the finite field of q/sup m/ elements. Let /spl alpha/_ be a basis of F/sub q//sup m/ over F/sub q/. Under the simplifying assumption (n,q)=1, it is shown that d/spl alpha/_(V), the q-ary image of V with respect to /spl alpha/, is decomposable into the direct sum of a fixed number of irreducible quasicyclic codes. This characterization allow us to obtain a lower bound on the minimum distance of d/spl alpha/_(V) by determining a lower bound on the number of not-null blocks in a typical codeword of d/spl alpha/_(V), for four specific subclasses of codes.