Efficient route discovery is a key analytical challenge in high-mobility IoT environments such as Vehicular Ad-hoc networks (VANETs), where frequent topology changes degrade communication reliability. This study leverages intelligent data analytics to compare heuristic (A, Greedy Best-First Search (GBFS )) and non-heuristic (Dijkstra, Bellman-Ford) algorithms within a grid-based vehicular network under varying densities. Using metrics such as Route Discovery Time (RDT), Route Discovery Messages (RDM), and Path Length (PL), we evaluate their performance in both sparse and dense topologies. Results show that in sparse networks, heuristic algorithms — particularly A — offer the best trade-of between discovery efficiency and path optimality. In contrast, GBFS sacrifices accuracy for speed, while non-heuristic methods incur higher overhead. In dense scenarios, A maintains superior performance, demonstrating scalability and robustness. These insights ofer valuable predictive analytics for designing routing protocols in dynamic IoT environments, enabling intelligent decision-making for connected vehicle systems.

In intelligent transportation systems (ITS), one of the main challenges is still finding efficient routes in highly dynamic vehicular ad hoc networks (VANETs). Even though traditional algorithms provide trade-offs between adaptability (responsiveness), computational complexity, and path optimality, their performance frequently deteriorates in situations that change quickly. In this study, a quantum-inspired routing algorithm based on Grover’s Search is evaluated in a traditional grid-based simulation setting. Through the simulation of quantum amplitude amplification, Grover’s method seeks to find effective paths with a shorter discovery time. Its performance is assessed against classical baselines—Dijkstra, A∗, and Greedy Best-First Search (GBFS)—under varying sparse traffic conditions, using three key metrics: Route Discovery Time (RDT), Path Length (PL), and Route Discovery Messages (RDM). Experimental results show that Grover’s Search consistently achieves lower RDT, indicating strong potential for latency-sensitive scenarios. Despite its higher message overhead, due to classical simulation of quantum processes, the approach underscores the promise of quantum-inspired routing and the need for further exploration of native quantum solutions in future ITS applications.

Optimal routing in Vehicular Ad-hoc Networks (VANETs) presents significant challenges due to the dynamic nature of vehicular movements. Cluster-based routing schemes have emerged as promising strategies to manage the scalability and efficiency of routing in VANETs. However, they often face limitations in coverage due to their inherent reliance on distributive route discovery methods. This paper introduces the Software-Defined networking-enabled cluster-based Evolving Graph Routing Scheme (SE-CEGRS), designed to address these coverage challenges by integrating the flexibility and centralized control of Software-Defined Networking (SDN) with the established Cluster-based VANET-oriented Evolving Graph (CVoEG) framework. Unlike traditional CVoEG, which employs a distributive approach with Evolving Graph Dijkstra (EG-Dijkstra) for both intra- and inter-cluster communications, SE-CEGRS maintains this strategy for intra-cluster scenarios but shifts to a centralized model for inter-cluster communications. Our evaluation demonstrates that SE-CEGRS significantly outperforms conventional CVoEG in inter-cluster communication scenarios by offering reduced discovery times, lower communication overhead, and shorter average path lengths. The findings underscore SECEGRS’s potential to enhance the efficiency and scalability of routing in VANETs, pointing toward a new direction for future research and development in vehicular communication technologies.

The Internet of Things (IoT) is witnessing rapid adoption across various fields due to the advances in wire-less communication and low-power devices. However, achieving energy sustainability in IoT applications is a non-trivial task. Energy Neutral Operation (ENO) has emerged as a promising approach to address this issue. To this end, duty cycle scheduling is a prominent power management approach to attain ENO. The dynamic nature of the IoT environment poses a great challenge to determine individual nodes' duty cycle due to intermittent energy supply and variation in QoS requirements and traffic intensity. Artificial Intelligence (AI) and Machine Learning (ML) techniques can enhance QoS performance when integrated with ENO solutions. Therefore, our work considers employing Reinforcement Learning (RL) to compute the duty cycle of individual nodes based on the network's energy and traffic conditions. This work evaluates the performance of the RL solution in the context of multi-hop communication. The results are compared to a modified version of a regression-based duty-cycling solution from the literature.

The General Chairs and Technical Program Chairs (TPC) are delighted to welcome you to the International Conference on Blockchain and Cryptocurrency (ICBC 2023) being held as an inperson event between May 1 and May 5, 2023, at the Sofitel Downtown hotel Dubai, UAE.

Unmanned aerial vehicles (UAVs) can be deployed and managed in a variety of applications with the help of flying ad hoc networks, or FANETs. However, the dynamically changing topology in FANET has raised significant challenges, mainly related to the insurance of security as a required service for the optimal performance of FANET networks. Blockchain technology has recently been used as an innovative solution to improve FANET security, due to its main characteristics such as transparency, decentralization, and tamper-proof nature. In this poster, we detail some current applications of Blockchain to secure FANET including military, surveillance, and Industry 4.0 services. Finally, we discuss some of the common issues associated with the application of Blockchain on FANET networks.

In this paper, we explore the benefits of cooperative diversity for Wireless Sensor Network (WSN). Relays are uniformily distributed in$L$hops and$N$branches between the sensor nodes SN and the base station BS. Three cooperative routing protocols are considered, namely: one hop, suboptimal and optimal routing protocols. One hop routing chooses the best relay in each hop. Optimal routing selects the best path between SN and BS. Suboptimal routing decomposes the network into$K$subnetworks and selects successively the best path in each one. We derive the outage probability, the Packet Error Probability (PEP) and the queening delays spent in the system for each protocol.

Long Range (LoRa) systems have received a lot of interest recently from the scientific community as well as for commercial application due to their ability to transfer data across large distances at a relatively low energy cost. In this paper, we derive the delay, the throughput, the Bit Error (BEP) and the Packet Error Probabilities (PEP) of LoRa for Rice channels. We derive packets' waiting time and total delay for LoRa in the presence of a Rice channel. Delay analysis of LoRA in the presence of Rice channel is not available in the literature.

Cooperative diversity enhance system reliability and impact its global performance. In this paper, we consider cooperation within a Wireless Sensor Network (WSN). We implement three cooperative routing protocols: One-Hop (OHRP), Optimal (ORP) and Suboptimal Routing (SORP) for WSN with energy harvesting using Radio Frequency (RF) signals. We derive the the Outage Probability (OP) of the system and we study the effect of the harvesting duration. Simulation results showed that optimal routing offers the lowest OP while suboptimal routing offers a good compromise between computational complexity and performance.

LoRa (Long Range) technology has recently attracted interest in a wide number of applications due to its extensive communication range and low power consumption. In the other hand, cooperative communication has become a potential technique for the effective use of communication resources by enabling end devices to work together for data transfer. The present work focuses on enhancing LoRa network’s performance through cooperative relaying. We propose a dual-hop Decode and Forward (DF) relaying LoRa system with best relay selection. We compute the Bit and Packet Error Probabilities (BEP and PEP) for Rayleigh channels with arbitrary position of relay nodes. We also compute the expected waiting and sojourn delays when packet arrival follows a Poisson process.

This paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability.

VANET nodes are characterized by their high mobility and they exhibit different mobility patterns. Therefore, VANET clustering schemes should take into consideration the mobility parameters among neighboring nodes to produce relatively stable clustering structure. This paper proposes a novel cluster head selection Fuzzy Logic-based k-hop distributed clustering scheme for VANETs. This scheme considers the safe inter-distance between vehicles as one of important metrics for cluster head selection. Our contribution deals better with the scalability and stability issues of VANETs and can achieve a high stable cluster topology compared to other schemes. These features make the proposed scheme more suitable for VANETs networks. Simulation proves the effectiveness of the new proposed scheme to create stable clusters by reducing re-clustering overhead and prolonging cluster lifetime compared to other existing clustering schemes for VANET.

The vast distribution of low-power devices in IoT applications requires robust communication technologies that ensure high-performance level in terms of QoS, light-weight computation, and security. Advanced wireless technologies (i.e. 5G and 6G) are playing an increasing role in facilitating the deployment of IoT applications. To prolong the network lifetime, energy harvesting is an essential technology in wireless networks. Nevertheless, maintaining energy sustainability is difficult when considering high QoS requirements in IoT. Therefore, an energy management technique that ensures energy efficiency and meets QoS is needed. Energy efficiency in duty cycling solutions needs novel energy management techniques to address these challenges and achieve a trade-off between energy efficiency and delay. Predictive models (i.e., based on AI and ML techniques) represent useful tools that encapsulate the stochastic nature of harvested energy in duty cycle scheduling. The conventional predictive model relies on environmental parameters to estimate the harvested energy. Instead, Artificial Intelligence (AI) allows for recursive prediction models that rely on past behavior of harvested and consumed energy. This is useful to achieve better precision in energy estimation and extend the limit beyond predictive models directed solely for energy sources that exhibit periodic behavior. In this paper, we explore the usage of a ML model to enhance the performance of duty cycle scheduling. The aim is to improve the QoS performance of the proposed solution. To assess the performance of the proposed model, it was simulated using the INET framework of the OMNet++ simulation environment. The results are compared to an enhanced IEEE 802.15.4 MAC protocol from the literature. The results of the comparative study show clear superiority of the proposed AI-based protocol that testified to better use of energy estimation for better management of the duty cycling at the MAC sublayer.

In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.

This publication was made possible by NPRP grant #10-0102-170094 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. In this paper, we investigate the physical layer security of a wireless communication with a reconfigurable intelligent surface (RIS) and a full-duplex (FD) legitimate receiver over Rayleigh fading channel in the presence of an eavesdropper. In addition, we examine the scenario where the FD receiver is sending a jamming signal while receiving the required message to confuse the eavesdropper. We derive closed form expressions of the intercept probability of two scenarios with and without jamming signal and we also use the strictly positive secrecy capacity (SPSC) metric to gain more insight on the security of our proposed system. To illustrate the analytical analysis, numerical results are provided under different network parameters to study the effect of RIS and jamming technique on the security of FD receiver.

In this paper, we consider a downlink cooperative NOMA system where both Decode and Forward (DF) and Amplify and Forward (AF) strategies are studied in the presence of a malicious eavesdropper. First, the secrecy outage probability (SOP) is studied when users' ranking is based on either instantaneous or average channel gains. Next, the strictly positive secrecy capacity (SPSC) is derived for users ranking based on average and instantaneous channel gains. Under this setup, we also investigate the improvement of the system secrecy using optimal power allocation (OPA) coefficients. Numerical and simulation results are provided and demonstrate that Instantaneous channel gain users' ranking provide better results and its further improved with the use of OPA.

Outdoor air quality monitoring (OAQM) is of great importance to human health and environment. Engineers and researchers are increasingly focusing their efforts on the design of real-time OAQM systems using wireless sensor networks. This paper presents an OAQM system enabling measurement of CO2, CO, Cl2, ambient temperature, and relative humidity. In our system, remote users usually employ a local gateway to connect wireless sensor nodes to the external world. In this work, the gateway processes the collected air quality data and transmits the useful data to end-users through a web-server. The nodes are self sustainable as they are equipped with a solar panel and a rechargeable Lithim-ion 2W 3.7V battery.

The Internet of Things (IoT) has the potentialities to advance the concretisation of the smart world through a wide interaction between the human society with its physical environment. This interaction is enabled by Wireless Sensor Network (WSN) used to identify and to interpret contexts. However, the IoT and WSN have intrinsically challenges, particularly Network Lifetime (NL) maximisation. In this context, researchers have developed power-aware mechanisms for WSN and IoT. These mechanisms delay the battery depletion of a sensor node and extend the NL to ensure continuous full operational network activity. Accordingly, in this paper, we review and discuss various recent contributions related to nature inspired power-aware wake-up scheduling approaches. Furthermore, we present a comprehensive taxonomy of the various nature-inspired wake-up schedule mechanisms. Besides this paper provides for the reviewed approaches usability contexts.

In all outdoor long haul air quality monitoring and mapping applications, sensing accuracy, the sustainability of telemetry, and resilience in node systems and operation are major challenges. The severity of these challenges varies depending on the moderateness and harshness of the climate of observation. In this work, an autonomous environmentally powered, sensors self-diagnostic/calibration, and context-aware IoT-based telemetry for multi-variable sensing node is being proposed. In this node, photo-voltaic and piezoelectric energy harvesters contributed to self-calibration and sustainable measurement of temperature (in °C), humidity (in %), pressure (in bar), geo-position (in NMEA format), volatile organic compounds-VOC (in ppm), particulate matter PM (in ppm), ozone (in Dobson Unit), Carbon mono-oxide (in ppm), Nitrogen dioxide (in ppm), and Sulphur dioxide (in ppm). Results have shown that the proposed system worked autonomously for days and optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University.

This paper investigates the physical layer security of a full-duplex (FD) Decode-and-Forward (DF) NOMA network assumed to undergo independent and identically distributed Nakagami-m fading. Herein, we provide a secrecy performance analysis for the case of multiple external eavesdroppers in both colluding and non-colluding wiretap scenario. Our analysis introduces novel expressions of the secrecy outage probability (SOP) as well as the strictly positive secrecy capacity (SPSC) for the proposed network. Finally, numerical analysis with simulations are given to illustrate the validity of the obtained theoretical results and to highlight the impact of multiple eavesdroppers on FD NOMA systems.

Due to the successive interference cancellation (SIC) technique, Non orthogonal multiple Access (NOMA) users have the capability of decoding the other paired users sharing the same resource bloc. As a result, NOMA technique is not only exposed to external eavesdropping, but also to internal eavesdropping which raises security risks regarding NOMA users' privacy. Therefore, this research aims to analyse a Multi-antenna cooperative NOMA system where both Decode and Forward (DF) and Amplify and Forward (AF) strategies are studied. In this context, we investigate the secrecy performance of our system with both internal and external eavesdropping scenarios. Closed-form expressions of the secrecy outage probability (SOP) and the strictly positive secrecy capacity (SPSC) metrics are derived. Numerical results are given to illustrate the validity of the obtained theoretical results.

In this paper, we derive the Secrecy Outage Probability (SOP) and Strictly Positive Secrecy Capacity (SPSC) of cooperative Non Orthogonal Multiple Access (NOMA) in the presence of K Decode and Forward (DF) relays and Rayleigh fading channels. We also optimize the powers allocated to far and near users to maximize the SPSC or minimize the SOP. Our results are valid for any number of relays with arbitrary positions.

Non-performing assets (NPAs) or bad loans limit the banks from lending to new borrowers. As a result, this would slow down the credit utilization that ends up lowering the deposit interest rates and raising the lending rates, thus, discouraging authentic borrowers. This paper presents a Hierarchical Timed Colored Petri Net (HTCPN) based model that simulates an intelligent agents system which automates credit monitoring and follow-ups by relationship managers (RMs) in bank branches. Efficient follow-ups help the RMs track the borrower's repayment and assess the current values of their counter-assets regularly. In case any loan is kept unattended by the responsible RM, an automated reporting escalation is initiated according to an escalation matrix when due checking time is reached. The HTCPN helped model the expected behaviour of the intelligent agents system used for loan monitoring and also allowed to model a scoring system that is used to monitor the performance of the RMs in handling the loans under their authority and the punctuality of their actions. The performance evaluation through cpn tools simulation resulted in performance ranking and scoring of the RMs, and helped collect statistics on the overall performance of the bank that pointed out to the stages and locations where the flaws are more severe thus providing decision support utilities.

The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset.

In this paper, we investigate the secrecy performance of an Amplify-and-Forward (AF) relay in a cooperative NOMA system. The AF relay ensures the transmission of the signal in the presence of one passive eavesdropper where all channels are assumed to undergo a Rician fading. In this context, analytical and asymptotic expressions for Secrecy Outage Probability (SOP) are derived. To verify our theoretical analysis, numerical results are presented, showing the effect of different Rician factors on secrecy performance.

Wireless Sensor Networks (WSN) are widely deployed to maintain Structural Health Monitoring of Water Pipeline System (WPS). Accordingly, it is imperatively important to ensure reliable communication between sensor nodes deployed in harsh environment to allow a continuous data collection and processing. In this context, we propose and implement an energy efficient solution that enables a seamless interconnection between sensor nodes, and trusty data transmission in order to maximize the network lifetime. After a clustering step, a Data Redundancy Elimination technique is applied to remove redundant data at each cluster head. This operation is followed by a data fusion algorithm based on Dempster-Shafer evidence theory at the Base Station. This scheme is proposed with aim of reducing the size of data carried by the network and consequently save on energy consumption. This results in improved WSN lifetime and more accurate WPS systems.

Water Pipeline Monitoring System (WPMS) is extremely important considering the several pipeline damages and the various hydraulic failures that cause a critical water loss. In this context, Cognitive Water Distribution System integrates Internet of Things (IoT) technology, based on smart sensors, actuators and connected objects, with a reliable Big Data processing for smart and robust Structural Health Monitoring (SHM) of pipelines. In this paper, we propose a cognitive IoT-based architecture where we used Apache Spark framework to maintain a real time processing of the large amount of collected data. This efficient processing of measured data and its correspondent calculated values simplify the transient simulations and leak detection and make it faster and easier.

Hydraulic failures in Water Pipeline System (WPS) can cause catastrophic environmental hazards. Wireless Sensor Networks (WSN) are greatly deployed to maintain a Structural Health Monitoring of pipeline and supervise the WPS. Since, its implementation increases significantly, its energy consumption represents a critical challenge that should be imperatively investigated in order to ensure an efficient and seamless interconnection between sensor nodes. In this context, the data aggregation techniques are well-designed and various smart algorithms are developed to reduce the quantity of transmitted data and to minimize the energy consumption. In this paper, we combine between data aggregation and bio-inspired clustering algorithm in order to improve the WSN Lifetime.

Internet of things has the ability to control and impact our physical world through technology by combining data collected from sensors and databases along with mining computational intelligence and analytics within IoT platforms. This is leading to a new generation of IoT solutions based on cognition and intelligent reasoning and known as cognitive IoT that will to paving the way to the deployment of new powerful tools. However, this cognition is susceptible to heavy risks related to security and privacy risks and vulnerabilities. In this context, this study will identify cognition and computing intelligence associated to IoT and highlights possible threats and attacks.

Network lifetime is the main issue of wireless sensor networks and IoT solutions in real world application. Sensors cannot have an infinite lifetime without battery recharge or replacement. Energy harvesting, from environmental energy sources, is a promising technology to provide sustainable powering for WSN. However, based on harvesting opportunities, nodes power may alternate between two states: a state with sufficient residual power and another with shortage in power. Hence, it is paramount to develop robust networking platforms that are energy-harvesting-aware and that support low-energy consumption and data integrity in a noisy, variable environment. In this paper, we present the EAMP-AIDC protocol, an energy aware MAC protocol for EH-WSN based on individual duty cycle optimization. It takes into consideration nodes' residual energy and application and data requirements in order to define individual dynamic duty cycles (Active and sleep periods) that allow to create a balanced load in term of cooperative data relaying tasks and in terms of energy consumption between the different participating nodes so as to ensure continuous network operation. The proposed protocol was evaluated using the network simulator OMNET++ and was compared to the standard IEEE 802.15.4 MAC. The results showed that EAMP-AIDC protocol outperformed the IEEE 802.15.4 standard in term of better energy consumption, increased survivability in energy savings and in guaranteeing continuous operations.

This paper introduces PSCAN, a port scanning-based network covert channel that violates non-discretionary system security policy that does not allow data transfer from a given process (the sender) to another given process (the receiver). Using PSCAN, the sender opens and closes network ports in a way that encodes covert data. The receiver performs a synchronized port scanning procedure on the sender's host to determine which ports are open and which ones are closed then decodes the data. The paper defines the covert channel and analyzes its data rate, stealthiness, and robustness. In addition, the paper investigates countermeasures against the channel.

Abstract Technology has revolutionized medical practices by enabling more convenient and non‐intrusive monitoring of patient's health, leading to next generation ubiquitous healthcare (u‐healthcare). The exploitation of the Internet protocol version 6 addressing space along with the miniaturization of electronic devices has fostered providing interoperability and connectivity of wearable sensor devices in wireless body area networks to the Internet of Things. In this paper, we propose to integrate the IPv6 over low power wireless personal area network (6LoWPAN) to the u‐healthcare monitoring system architecture. The main objective is to study the feasibility of the 6LoWPAN‐enabled platform in real‐world scenarios dealing with medical data. The performance evaluation of this platform is carried out initially through simulations using OMNet++ and then supported by an experimental study using sensor motes and a customized micro‐computing unit. Performance metrics such as throughput, end‐to‐end delay, packet error rate, and energy consumption are investigated under acute health conditions, where patient's health information has to be sent continuously and at maximum rate to the care provider. The obtained results show that the proposed 6LoWPAN solution fulfills the main quality of service requirements of u‐healthcare applications. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT In this work, we present a new generic polymorphic routing protocol tailored for vehicular ad hoc networks (VANETs). Similar to the case of mobile ad hoc networks, the routing task in VANETs comes under various constraints that can be environmental, operational, or performance based. The proposed Polymorphic Unicast Routing Protocol (PURP) uses the concept of polymorphic routing as a means to describe dynamic, multi‐behavioral, multi‐stimuli, adaptive, and hybrid routing, that is applicable in various contexts, which empowers the protocol with great flexibility in coping with the timely requirements of the routing tasks. Polymorphic routing protocols, in general, are equipped with multi‐operational modes (e.g., grades of proactive, reactive, and semi‐proactive), and they are expected to tune in to the right mode of operation depending on the current conditions (e.g., battery residue, vicinity density, traffic intensity, mobility level of the mobile node, and other user‐defined conditions). The objective is commonly maximizing and/or improving certain metrics such as maximizing battery life, reducing communication delays, improving deliverability, and so on. We give a detailed description and analysis of the PURP protocol. Through comparative simulations, we show its superiority in performance to its peers and demonstrate its suitability for routing in VANETs. Copyright © 2011 John Wiley & Sons, Ltd.

In a dynamic network environment under heavy traffic load, shortest-path routing algorithms, particularly attempt to adapt to traffic changes, frequently exhibit oscillatory behaviors and cause performance degradation. In this paper, we first analyze, the instability factors and their results in the multicast infrastructure, and then analyze the solutions leading to stability in switched networks. Subsequently, we propose a solution to the above routing problem, where the route computation is based on the actual load on the network links. We propose a fast and simple heuristic algorithm named the Cost-Delay Shortest Path (CDSP) algorithm, which attempts to strike a balance between the different QoS parameters. The CDSP algorithm is based on a simple path exponential selection function taking simultaneously both the cost and delay into consideration and allowing the network load to be distributed in an optimal way.

The fulfilment of guaranteed multiple criteria based quality of service (QoS) for multimedia applications servicing multiple subscribers is known to be a NP complete problem. Moreover, finding the multicast graph respecting the defined QoS requirements and minimizing network resources is also a NP-complete optimization task. The well-known greedy algorithm Mamcra, proposed in the literature, computes the set of shortest paths from a source to all destination, and then reduces this set to an efficient set of multicast routes, without compromising the requested level of QoS. A closer look at Mamcra shows that it does not exploit further simple but possible reductions of the multicast sub-graph. In this paper, we propose a taboo search algorithm, named TabooQMR, that is augmented by some meta-heuristics to improve the multicast sub-graph computed by the greedy algorithm Mamcra, and leading to a considerable improvement, as demonstrated by the simulation results.

Multimedia applications are expected to guarantee end-to-end quality of service (QoS) and are characterized by stringent constraints on delay, delay-jitter, bandwidth, cost, etc. In the litterature, we observe that Kruskal's algorithm is limited to minimal (maximal) spanning unconstrained tree. In this paper, we extend Kruskal's algorithm to incorporate the delay bound constraint. Consequently, we propose a novel algorithm, called EKRUS (Extended Kruskal), for constructing multicast trees. The EKRUS' distinguishing features consists in a better management of Kruskal's priority queues, and in the provision of edge priority aggregation. Preliminary results show that our proposed EKRUS algorithm performs as good as the best well-known algorithms (such as the DDMC, DMCTc algorithms) while exhibiting better complexity. Furthermore, we believe that EKRUS is well suited to actual implementation in networks requiring support for multicast communication.

We first propose a new proactive routing framework based on probabilistic decisions and a generic model to compute the existence probabilities of nodes and links. Then, we present a distributed algorithm to collect the cartography of the network. This cartography is used to instantiate the existence probabilities. Conducted simulations show that our proposal yields substantially better routing validity. Nonetheless, it amounts to much longer routes. We proposed then a bounding technique to overcome this side effect and defined two probabilistic proactive routing variants. Conducted simulations show that our proposed bounded probabilistic proactive routing schemes outperform conventional routing protocols and yield up to 66 percent increase in throughput.

The well-known greedy multicast routing algorithm Mamcra, proposed in the literature, computes the set of shortest paths from a source to all destinations, and then reduces this set to an efficient set of multicast routes, without compromising the requested level of QoS. In this paper, we propose a taboo search algorithm, named TabooQMR, that is augmented by some meta-heuristics to improve the multicast sub-graph computed by the greedy algorithm Mamcra, leading to a considerable improvement, as demonstrated by the simulation results.

This paper describes a new approach for routing in Mobile Ad Hoc Networks that is based on the concept of polymorphic behavior. This approach combines the benefits of proactive routing and probabilistic routing in time in a periodic manner in order to get the best of each approach at the appropriate

We propose in this paper an optimized, polymorphic, hybrid multicast routing protocol for MANET. This new polymorphic protocol attempts to benefit from the high efficiency of proactive behavior (in terms of quicker response to transmission requests) and the limited network traffic overhead of the reactive behavior, while being power, mobility, and vicinity-density (in terms of number of neighbor nodes per specified area around a mobile node) aware. The proposed protocol is based on the principle of adaptability and multibehavioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity

We propose in this paper, an optimized, polymorphic, hybrid multicast routing protocol for MANET. The protocol proposed is based on the principle of adaptability and multi-behayioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. This new polymorphic protocol attempts to benefit of the high efficiency of proactive behavior and the low cost of network traffic of the reactive behavior, while being power, mobility and vicinity density aware. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity.

We propose in this paper, a polymorphic, hybrid multicast routing protocol for MANET (mobile ad hoc networks). This new polymorphic protocol attempts to take benefit of the high efficiency of proactive behavior and the low cost of network traffic of the reactive behavior. The protocol is based on the principle of adaptability and multi-behavioral modes of operations. The proposed protocol is able to change behavior in different situations in order to improve certain metrics like the battery power. Extensive simulations demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity.

In this paper, we introduce a simple model for the enhanced distributed channel access (EDCA) mechanism under saturation conditions. This model captures the operation of the AIFS (arbitration inter-frame space) and contention window differentiation of the EDCA mechanism. Using this model, we analyze the throughput and delay performance of EDCA. The results of our analytical model are then verified using simulations.