This study aims to improve transportation safety by addressing a persistent source of road accidents: pavement anomalies such as potholes and cracks. We develop a comprehensive system that detects potholes and multiple crack types (e.g., alligator, transverse, longitudinal), estimates their size/severity, and is architected to transmit evidence and metadata to a cloud service to support timely action by authorities. Central to our approach is the Frame Span Interval (FSI), which imposes a temporal window (with spatial tolerance) so that consecutive detections of the same physical anomaly at a specific location are consolidated into a single event-preventing duplicate counts, enabling accurate real-time tallying, and stabilizing downstream tracking metrics. The prototype, built with a Raspberry Pi, camera module, and CNN-based model with a laptop host, demonstrates on-device inference and local logging; backend integration and notifications are future work. This integrated approach targets the mitigation of accident risk arising from roadway damage and supports safer road conditions for the broader community.

Drones are increasingly used in mission-critical environments such as border surveillance, military logistics, and infrastructure inspection. However, their flexibility also introduces cybersecurity risks, as adversaries can exploit them for malicious purposes. A promising defense technique involves using electromagnetic interference to selectively neutralize rogue drones—provided their communication parameters can be precisely identified.In this work, we propose a reinforcement learning agent based on Proximal Policy Optimization (PPO) to autonomously identify the frequency–power pair susceptible to interference for an unknown drone-controller system. The agent operates in a large search space, guided by an environment modeled with dynamic hot zones. It is designed to minimize probing steps and energy consumption, making it suitable for time- and resource-constrained defensive scenarios. Experimental results in a simulated environment show that the agent achieves 90% accuracy in identifying effective jamming parameters, demonstrating its potential as a real-time, adaptive cybersecurity countermeasure for drone-based threats.

The Internet of Things (IoT) has transformed how smart cities operate, significantly improving their inhabitants’ efficiency and overall quality of life. However, the massive volume of sensitive data generated by IoT devices presents challenges, including privacy concerns and communication overheads. Traditional centralized data processing can compromise privacy and require significant communication, leading to scalability problems and power drains. Federated Learning (FL) offers a solution by processing data locally and transmitting only the model parameters. Vanilla FL faces challenges in IoT environments due to latency, bandwidth constraints, and power drain. Hierarchical FL (HFL) effectively addresses these issues by hierarchically leveraging the processing capabilities of both cloud and edge servers to optimize resource utilization and efficiently minimize latency. This paper evaluates HFL using IoT-derived datasets, develops and implements the HFL framework for resource-constrained IoT systems, and conducts the first known HFL tests on relevant datasets to demonstrate its performance.

In recent years, the field of programming Unmanned Arial Vehicles (UAVs) has gained significant attention from researchers due to their substantial potential in various applications, including surveillance, inspection, and critical situations like examining buildings that are burning or collapsing. For this purpose, drones that have several sensors installed would be of good use in constructing a 3D map and localizing most of the objects within a certain area or room, which is also referred to as Simultaneous Localization and Mapping (SLAM). However, installing these sensors would harden the mission of the drone since it would mean more power consumption, more computations, and less navigation flexibility. For this reason, monocular visual SLAM has become the trend, which refers to using a sole camera to build the map and locate the objects in each scene. This approach introduces new challenges, one of the most crucial challenges is estimating the depth (i.e., distances within an image) of each scene from a 2D image. For this task, Deep Learning (DL) models have been considered as a solution for this problem, and with the continuous development in DL and the computational resources that can carry out the expensive training of DL models, it was shown that a 3D map reconstruction is possible utilizing 2D images. This work investigates the performance of a combination of different SLAM and depth estimation models implemented on a commercial drone. The main goal is to carry out a comparison between different methodologies of depth estimation that support monocular 3D SLAM algorithms.

Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system’s ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.

The time sensitivity of healthcare applications leads to the urgent need to process the data with minimum delay. The limited resources in end-user devices make offloading the tasks to the fog node to process them essential. Fog node can process the tasks near the end-user, which reduce the delay caused by offloading them into the cloud. Fog nodes in the fog layer can collaborate to process tasks. A balanced task schedule in the fog layer will make the fog nodes collaborate to reduce the response time. In this work, we start by computing a probability for each task to decide whether the task will offload to the fog master or not. This probability will allow the fog node to process some of the received tasks without the need for offloading. We only offload the tasks whose probability exceeds a threshold value to the fog master. The fog master run the Ant Colony Optimization algorithm to schedule the task to a suitable node in the fog layer. The simulation results show that the proposed load balancer provides better results in reducing the delay.

Federated Learning (FL) is a cooperative machine learning (ML) training method that involves several participants in the training phase. The learning phase of ML algorithms is done in a distributed manner where devices train a specific model based on their local data sets and send the updated model back to a centralized entity. Several benefits are gained by using FL compared to conventional centralized learning techniques, such as increased security and data privacy since participant devices only send back the updated model and not their data sets. Recently, this has become a hot research topic due to its advantages in terms of user privacy. Most of the works in this field consider only star networks where mobile devices communicate directly to the base station. Few mentioned multi-hop scenarios. In this paper, we present a performance evaluation of FL in multi-hop networks and study the effect of clustering devices on FL accuracy.

Manufacturing variations introduce features that distinguish radio transceivers even those of the same vendor. Such distinction is often referred to as radiometric signature and is found to be useful in conducting device authentication and crime forensics. Yet, radiometric signatures could constitute a privacy threat. Particularly, in the realm of wireless networks, an adversary may exploit RF fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show that RF fingerprinting could be a major tool for the adversary to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. We further develop a novel countermeasure to degrade the adversary's ability in exploiting RF fingerprinting. The proposed countermeasure is based on switching among preset communication protocols and employs adversarial machine learning to select the protocol for a transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.

The technological advances in smartphones have brought promising potentials to tracking systems. GPS and WLAN are widely used technologies in recent smartphones for positioning and communication, respectively. However, both GPS and WLAN have serious energy consumption problem. For large scale tracking systems, where tracked people are supposed to move in groups, the continuous usage of the individual smartphones GPS and WiFi is not an energy efficient solution. In this work, we propose a cooperative approach that exploits the coexistence of of multiple communication interfaces (WiFi and Bluetooth) in recent smartphones where nearby smartphones are cooperatively grouped to form a cluster. A cluster is a network of one dynamically selected cluster head and regular members that communicate locally via Bluetooth. The cluster head is responsible of positioning and sharing positioning information with the rest of cluster members. It, also, acts as a gateway between its regular members and the server. We present a clustering approach according to the available energy and the energy cost. Our approach doesnt require any modifications in the recent smartphones equipment. We have formulated the clustering construction problem as a mixed integer program using GAMS. The proposed clustering approach has been evaluated via extensive simulation experiments and the obtained results have been compared with the optimal solution which show very close performance.

Monitoring and tracking healthcare conditions of crowds is a challenging mission. Although, there are several existing techniques to monitor and track crowds, these techniques suffer a number of serious issues such as inaccurate identification, high energy consumption and high infrastructure cost. In this paper, we propose to use a low-cost solution without the need to build any infrastructure by exploiting the availability of Bluetooth and Wi-Fi interfaces in smartphones. We design a Smart Real-Time Healthcare Monitoring and Tracking System based on mobile clustering. This application can be used in large public events such as the annual Muslim pilgrimage (Hajj), festivals, airports, train stations, etc. The monitored crowd are grouped autonomously into small clusters (i.e. piconets). Each cluster collects its members' data via Bluetooth and then delivers it using WiFi/3G/4G. We have evaluated the proposed approach via simulation and a prototype using our developed API under different scenarios. Our approach demonstrates 74% drop in energy consumption compared to a contemporary approach and its ability to detect 96.4% of participants.

Wireless sensor network has proven to be a good candidate for many monitoring applications such as habitat monitoring, structural health monitoring, pipeline monitoring, etc. Pipeline monitoring is a challenging application where the sensors are placed in a linear topology. This linear topology requires careful attention in placing sensors to ensure robustness against anomaly, minimize the energy consumption and maximize the network lifetime. This paper investigates this problem by improving and evaluating the performance of two greedy node placement approaches. In contrast to existing work, we have validated experimentally the 31 power levels of CC2420 TelosB chipon and their corresponding transmission ranges. Having more power-level resolution yields less energy consumption and longer lifetime compared to traditional 8 power levels. Extensive simulation and real experiments have been conducted. The results demonstrate 23% extension in the lifetime when all power levels are adopted.

Deploying a networked set of robots is an effective way to serve applications in environments where human intervention is impossible or possess risks. For example, a team of robots can assist rescuers to map, navigate indoor hazardous areas in rescue operation. Collaboration among the robots is very essential in these applications in order to efficiently achieve the aimed goals in a timely manner. Realising such a collaborative operation autonomously in the absence of GPS services is a challenge. This study tackles this challenge assuming sensors/landmarks are present in the deployment area. Each sensor/landmark requires a specific number of robots to perform certain tasks. A spatial–temporal coverage solution is pursued to maintain connectivity and overcome the shortage of available robots. Dynamic coverage problem is formulated as potential fields where landmarks and nodes exert virtual forces among each other based on coverage demand and overlapped area. The proposed approach has been validated through extensive simulation using NS3 simulator and real experimentation using EV3 robots. The proposed approached has shown maximal task satisfaction compared with random waypoint and very close behaviour compared with a centralised approach (Hungarian method).

Wireless Sensor Networks (WSNs) is an evolving technology used in a diverse set of applications. WSN might be applied in isolated or hazard areas where it is hard and costly to reach the nodes. Maintaining the sensor nodes in WSN is generally a complex and difficult task especially when the size of the network is large. Energy maintenance of the sensor nodes is one of the challenging tasks in WSN. Supply energy for WSN using energy harvesting is a promising approach. It enables the sensor nodes to exploit the renewable ambient energy sources such as solar, temperature, sound and pressure to achieve a continual energy source. AODV is a routing protocol that was adopted by Zigbee for mesh network. However, not much work exists in literature that explores deeply the capabilities of AODV under energy harvesting. In this paper, extensive simulation experiments have been conducted using ns-3 to study the behavior of ADOV for energy-harvested and limited-battery WSN assuming total supply energy is the same for both approaches. We have compared AODV for both approaches using different performance metrics.

Robotics network is an effective way to deploy in areas where human intervention is impossible or possess some risks. In rescue operations, for example, robots can be used to help in discovering bodies under the rubbles or even assist the injured. One of the main challenges in these applications is how to deploy the robots in the absence of GPS services and without central coordination. In this paper, we tackle such a challenge in scenarios where landmarks are present in the deployment area. The problem is modeled by defining the actor coverage based on the individual landmarks. To deal with actor count limitation, a spatial-temporal coverage solution is pursued where some actors juggle between landmarks as needed. We formulate such dynamic coverage problem using Potential Fields where landmarks and actors exert virtual forces based on coverage demand and overlap. Extensive simulation experiments have been carried out using NS3 to evaluate the proposed approach and to compare it to Random Waypoint based dynamic coverage method. The simulation results validate the distinct performance of the approach in term of demand satisfaction and average traveled distance.

Smart grid is the future vision for supplying energy. It integrates the advances in power engineering as well as information technology and telecommunication in one cohesive network. However, this vision poses several challenges; one of the key challenges is the continuous monitoring of the state of every component (e.g. voltage, current, temperature, energy consumption, energy prices, etc.) in the system. Wireless sensor network is a promising candidate for fulfilling this task. This work suggests a new realistic approach for providing energy for the deployed sensor nodes using mobile charger. The aim is to sustain the operation of the sensor network in terms of data transmission and coverage. The proposed approach integrates the cluster formation process using LEACH-C with the recharging process by defining the required emitted energy as well as the locations to be visited by the mobile charger. The main objectives are to maximize the nodes battery utilization, distribute power effectively from the energy harvester, and optimize the distance between mobile charger and sensor nodes. We tested our approach using NS2 simulator. The simulation results have shown the achievement of the above objectives.

The standard IEC 61850 is considered as a candidate for communication standard for smart grid applications. The delay performance is one of the critical issues that were specified by IEC 61850. However, a few works have been done to evaluate the delay performance under wireless networks. This paper presents the modeling and simulation of IEC 61850 substation automation system (SAS) under wireless and hybrid networks. OPNET is used to model and simulate the Intelligent Electronic Device (IEDs) for three types of messages: GOOSE, the Raw Data Sampled Values and the Interbay trip messages in Substation Automation System.

The contemporary routing algorithms were built and tested under the assumption of uniformly distributed sensor nodes. However, this assumption is not always true. In some industrial applications and due to the scope of the ongoing monitoring process, sensors are installed and condensed in some area, while they are sparse in other areas. In this work, we investigate the impact of sensor node deployment distributions on the performance of different flavors of LEACH routing algorithm. Further, we study the impact of base station location, and different data loads. A comparison between LEACH and LEACH-C is conducted. The results show that different sensor distributions leads to significant differences in network performance. Furthermore, it shows that LEACH-C out performs LEACH almost in all distributions variations and base station locations.

This paper presents the outcomes of experiments performed to acquire data and to evaluate various parameters to: (1) assess the feasibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes, and, (2) characterize commercially available PowerCastTM energy harvester. The parameters of interest are: (1) Ambient RF power along several bands, (2) Time, Tc, taken by PowerCastTM energy harvester for charging, and (3), The received signal strength indicator (RSSI) captured by PowerCast™ energy harvester. The former measurements are taken at different locations inside King Fahd University campus. The latter two quantities were computed from data acquired at various coordinates of the harvester relative to the transmitter and the source of energy. The data is acquired in indoor and outdoor scenarios. The acquired data was processed in MATLAB and it was concluded that: (1) The ambient RF power in not sufficient to energize the commercially available PowerCast™ RF power harvester which has a specified requirement of at least -10dBm power to successfully harvest RF energy. (2) The outdoor experiments suggested that Tc is directly, while RSSI is inversely proportional to the radial distance from the power transmitter. The two quantities are also significantly affected by the variation of azimuth and elevation of harvester w.r.t. the transmitter. (3) It is observed in the indoor experiments that trends of Tc and RSSI are not as regular as those in the case of outdoor experiments. Experimental procedure and all of the analyses are documented in detail so that it can be a useful reference for researchers working in this area.

In Wireless Sensor-Actor Networks (WSANs), sensors probe their surroundings and send their data to more capable actor nodes in order to execute an application task. The actors' response is mostly collaborative and requires them to coordinate their operation. Therefore, a strongly connected inter-actor topology would be necessary at all time and tolerance of an actor failure becomes a design requirement. Autonomous repositioning of actor nodes has been deemed as an effective recovery strategy. This paper presents a Least-Movement Topology Repair (LeMoToR) algorithm. LeMoToR is a distributed scheme that relies on the local view of a node about the network. To restore connectivity, LeMoToR strives to relocate the least number of nodes and reduce the traveled distance and message complexity. Unlike contemporary schemes that maintain 1 or 2-hop neighbor lists, LeMoToR utilizes existing path discovery activities in the network in order to know the structure of the topology and avoids imposing additional pre-failure communication overhead. The simulation results validate the effectiveness of LeMoToR.

In most applications of Wireless sensor and actor network it is important to sustain connectivity among all actors at all times. When an actor fails the inter-actor topology may get partitions into disjoint blocks and the application may be negatively impacted. Tolerating the actor failure and restoring the lost connectivity need to be performed while imposing the least overhead on the individual actors. In this paper a Least Distance Movement Recovery (LDMR) algorithm is proposed. LDMR is a distributed approach that exploits non cut-vertices actors in the recovery process. The idea is for a set of direct neighbours of the failed node to move toward the position of the failed node while its original position is replaced with the nearest non cut-vertex actor. The recovery process starts with the search phase where each neighbour broadcasts a message containing the failed node ID, neighbour node ID and, Time-To-Live (TTL). When a neighbour receives responses, it chooses the best candidate based on a certain criteria (e.g. distance). We compare our approach with Recovery through Inward Motion (RIM) which depends only on cascaded movements. Extensive simulation experiments are carried out to validate the performance.

Oil and Gas industry shows great interest in deploying wireless sensor network for monitoring smart wells. The deployed sensors are very expensive and it is important to minimize the deployment cost while satisfying the coverage and connectivity requirements. In this work, we present a coverage-connectivity model for optimal deployment of sensors and relays. The monitored field is presented as a grid of 2D bounded space. We develop an Integer Linear Program (ILP) for minimizing the total cost of the sensors and relays for complete coverage of predetermined critical points in the monitored field and maintain and connectivity of the sensors with a central processing unit. The proposed model lends itself easily to a wide range of industrial applications. A numerical example is presented.

This paper is an attempt to understand better the impact of users mobility and primary appearance on the spectrum handoff rate and connection reliability for the Cognitive Radio (CR) Ad hoc Network. Spectrum handoff is a new type of handoff caused by the appearance of the original user of the spectrum or simply by the movement of a cognitive user to an area where its currently used band is in use. We exploit the spectrum sensing capability in each CR and we propose three random techniques to access free channels while avoiding/minimizing the hidden terminal problem. Extensive simulation experiments have been conducted to evaluate the system performance, in particular, handoff rate, session continuity distribution, and missing handoff. Furthermore, a simple queuing model is suggested. Finally, simulation results are compared with analytical results which show a good matching.

In Wireless Sensor-Actor Networks (WSANs), actors collect sensor readings and respond collaboratively to achieve an application mission. Since actors coordinate their operation, a strongly connected network topology would be required at all time. In addition, the path between actors may have to be capped in order to meet latency constraints. However, a failure of an actor may cause the network to partition into disjoint blocks and would thus violate such connectivity goal. One of the effective recovery methodologies is to autonomously reposition a subset of the actor nodes to restore connectivity. Contemporary schemes rely on maintaining 1 or 2-hop neighbor lists and predetermine criteria for node's involvement in the recovery. However, 1-hop based schemes often impose high node relocation overhead. In addition, the repaired inter-actor topology using 2-hop schemes often differs significantly from its pre-failure status and some inter-actor data paths may get extended. This paper presents a Least-Disruptive topology Repair (LeDiR) algorithm. LeDiR relies on the local view of a node about the network in order to devise a recovery plan that relocates the least number of nodes and ensures that no path between any pair of nodes is extended. LeDiR is a localized and distributed algorithm that leverages existing path discovery activities and imposes no additional pre-failure communication overhead. LeDiR is validated through simulation and is shown to outperform existing schemes.

In Wireless Sensor and Actor Networks (WSANs) a connected interactor topology is desirable in order for the deployed actors to work collaboratively. If a critical actor fails causing the inter-actor network to get partitioned into disjoint segments, the other actors close to the faulty node often exploit their mobility to autonomously restore the lost inter-actor connectivity. However, such a solution focuses on resource efficiency and assumes no constraints on the mobility of actors which can be impractical in the real scenarios. In addition, since actors need to carry out tasks to meet the application level requirements, unconstrained movement of actor(s) to restore interactor connectivity can cause a major failure at the application level. This paper presents C2AM; a recovery algorithm that factors in application level constraints on actor's mobility while restoring the network connectivity. In addition to considering physical level requirements, C2AM accounts for application level concerns as well in order to avoid major disruptions to ongoing missions. Simulation results have validated the effectiveness of the algorithm in maintaining both objectives.

There are basic design elements in Wireless Local Area Networks (WLAN) architecture to provide good service for the users like locations, number and transmission power of the Access Points (APs), also known as Base Stations (BSs). The number of deployed stations has a direct impact on the cost of the WLAN. Moreover, the intercell interference is affected by the number of APs and their locations. It is, therefore, always desirable to minimize the number of stations needed to achieve the required Quality of Service (QoS). Conventionally, it is the network planner's task to manually choose locations and parameters of APs based on prior experience. This manual approach is highly inefficient and time consuming. For this purpose, automatic network planning can substantially reduce the overhead cost of the network, minimize the intercell interference and maximize the QoS. In this work we propose a new placement technique for WLAN APs based on well-known 2-D convolution. The objective of the placement problem is to cover the whole area with minimum number of access points taking into consideration the environmental features of the intended area such as radio propagation model and antenna patterns. The proposed approach shows flexibility and efficiency in this placement problem.

The interaction between transport layer and MAC layer is very complex. Yet, understanding this relation is essential for devising an efficient protocol that maximizes the network throughput in a fair manner and minimizes the access delay. The effort in this paper focuses on examining all possible interference modes arising among two interfering Bi-directional traffic like TCP for one-hop connections under a Two-Disc model of interference. We believe that this work is the first in the literature that analyzes the TCP similar traffic flows under all these possible link interactions. Using ns-2, we run extensive simulation experiments for all possible link configurations. The results identify several performance groups. In particular, we are able to identify which configurations will lead to unfairness problem.

Trust-aware routing in wireless sensor networks (WSNs) is a crucial problem that has drawn the attention of researchers. The motivation for tackling this problem arises directly from the highly constrained nature of a WSN and its easy exposure to insecure conditions. In this regard, reputation-based solutions are used to provide trust-aware routing. However, this approach requires that a node needs to continuously monitor its environment to detect misbehaviour events. This is considered to be a costly operation for WSN nodes because of its resource scarcity. Here, the authors propose a reputation system-based solution for trust-aware routing, which implements a new monitoring strategy called an efficient monitoring procedure in a reputation system (EMPIRE). EMPIRE is a probabilistic and distributed monitoring methodology that tries to reduce the monitoring activities per node while maintaining the ability to detect attacks at a satisfactory level. The proposed procedure has been evaluated using the Monte Carlo simulation. New evaluation methodologies are introduced to test and explore the efficiency of our proposed procedure. Simulation results of the reputation system show that reducing monitoring activities with EMPIRE does not have a significant impact on system performance in terms of security.

Wireless networks have unreliable channels that experience bursty and location-dependent errors. Several fair queuing algorithms have been proposed in order to provide QoS in presence of errors in a fair manner. However, most of these algorithms are unpractical as they require perfect channel predication or do not work well with the Link Layer. Wireless fair queuing with retransmission (WFQ-R) algorithm was recently suggested to address these problems by penalizing flows that use wireless resources without permission in the link layer. However, the WFQ-R algorithm is based on stop-and-wait LLR scheme which also costs the network extensive delay and low utilization. In this paper, a new wireless fair queuing based on the WFQ-R algorithm is proposed to work with the window-based error control schemes in the link layer. The proposed algorithm has shown outstanding results compared with WFQ-R in terms of lower queuing delay, better throughput and fairly allocated resources.

We present an effective end-to-end mechanism of interaction between link layer and TCP over wired/wireless network. The motivation for adopting this method is to overcome both sources of error, namely; channel errors and congestion errors. This mechanism is adaptive in nature where channel encoding parameters (i.e. coding rate) will vary based on the deterioration level in the channel conditions. It is worth to note that whatever TCP flavor is implemented at the end systems, the proposed algorithm will not impose any change to it. NS2 is used to simulate the heterogeneous networks and implement the proposed adaptive method. Extensive simulation experiments have been conducted to evaluate the performance of the proposed approach and examine against fixed FEC approach. The simulation results have proven the ability of the proposed scheme to track the changes in the wireless channel and provide the necessary level of redundancy for FEC coding. Hence, it optimally uses the available resources and at the same time it achieves the maximum possible throughput.

The level of information conveyed to the sink in a wireless sensor network is very vital to the correct operation of the ongoing application. In WSN, Using the absolute throughput, the number of data packets delivered to the sink, is not accurate. Since many packets are resulted from the aggregation process of many "raw" packets collected from the concerned sensed area. In this paper, we propose an entropy-based throughput metric to measure the performance of the hierarchal routing protocols for WSN. In the proposed metric, the information delivered to the sink is calculated instead of the number of data packets delivered to the sink. This method will lead to fair comparison and evaluation of different routing protocols as well as more informative decision by the sink. We use the proposed metric to compare the performance of two well-known routing protocols; LEACH and EAD.

Much research is going on to enhance the distributed coordination function (DCF) of the IEEE 802.11 as to support quality of service (QoS) requirements needed by real time services. This is achieved by enhancing the throughput and delay characteristics of the DCF. The throughput of DCF degrades in high loaded situations. Most analyses study the network in a saturation state where each station is always transmitting. In this paper, we propose and evaluate a novel and simple algorithm to enhance the backoff procedure of DCF and provide QoS differentiation. The new algorithm attempts to adoptively control the DCF contention window in order to alleviate congestion through an easy to set parameter referred to as q. In our study, we evaluate the DCF and the new under non saturation condition where each station receives frames from upper layers according to a Poisson process. Simulation results indicate that better throughput and delay figures are obtained using the proposed algorithm. Moreover, the proposed algorithm shows less sensitivity to the number of active nodes especially for low number of active nodes. This feature is very important for WLAN service provider.

This paper studies the uplink performance of multi-operator WCDMA cellular networks. Allocating the resource of a cell between different operators and the deployment of a QoS-aware uplink admission control for each operator is studied and simulated. The contribution of this work lies in granting higher priority to soft handoff calls. We introduce queuing techniques and the idea of 'soft guard channels', which is represented by reserving a small fraction of the cell load for the higher priority calls. The performance of this admission control with different scenarios is studied. The Grade of Service (GoS) is considered here to evaluate the system performance. Based on the simulation results we can conclude that this algorithm can reduce the dropped soft handoff calls and improve the over all system performance. Hence, this algorithm is able to satisfy the network operator by allowing more traffic (more revenue) and at the same time satisfying the user by guaranteeing lower handoff call drop.

The next wireless generation is calling for seamless integrated networks where users can roam freely among different 4G network operators. To accomplish this challenging objective, an effective resource sharing mechanism among 4G multi- operator networks should be implemented. This paper investigates a new method of dynamically prioritized resource allocation for multi-operator WCDMA networks. This method is based on assigning uplink shared resources among users from different operators based on their current priority level which is a function of their current assignments as well as their previous assignments. More, the system under consideration assumes both original calls besides handoff calls. However, to ensure higher priority to soft handoff calls, queuing and 'soft guard channels', are introduced. The performance of this proposed allocation method is studied using simulation. It has been examined against the fixed resource allocation method under different traffic load situations. The Grade of Service (GoS) and the expected carried traffic are considered here to evaluate the system performance. The simulation results have shown the outstanding performance of the dynamic method compared with the fixed one. Moreover, the dynamically prioritized resource allocation method has shown fairness in assigning resources among networks operators' customers.

CDMA network is well known for its interference- limited performance. Minimizing the interference and achieving high throughput along with fairness is still the objective of many research efforts. This paper investigates this issue through exploring the relation between scheduling interval and power assignment on the performance of downlink slotted CDMA networks for different scheduling criteria. The proposed scheduler is a power based scheduler. It shall assign "channels" to contending users based on their power requirements and QoS constraint. The scheduler will choose first the user that requests the minimum power level as far as its maximum delay threshold is not reached and it continues in serving minimum power users until all allocated power or set of codes is exhausted. The scheduler is examined for different scheduling intervals, delay thresholds and different levels of burstiness. We compare through system level simulations of the downlink channel the enhancements provided by the proposed scheme with Power Earliest Deadline First (PEDF). The simulation results show outstanding performance for the proposed algorithm in terms of higher throughput, much lower packet delay and comparable fairness index figures under a wide range of traffic loads.

Abstract Radio resource management (RRM) is vital for the next generation wireless networks. RRM comprises many functionalities and this paper focuses on the investigation of the performance of several adaptive call admission/congestion control policies based on a window‐measurement estimation of the status of the buffer at the base station under the hybrid TDMA/CDMA access scheme. In our study, we interrelate the physical limitations of the base stations (i.e. the number of transmission and reception modems), call and burst level traffic, instantaneous buffer conditions and end‐to‐end bit error performance in one queuing problem. Subsequently, a window‐measurement estimator is implemented to estimate the likelihood of buffer congestion at the base station. Accordingly, the traffic loads shall be controlled. We use event‐driven simulation to simulate the multimedia integrated CDMA networks where heterogeneous traffic users are multiplexed into a simple TDMA frames. The simulation results show outstanding performance of the proposed call admission/congestion control policies in guaranteeing QoS requirements. Copyright © 2005 John Wiley & Sons, Ltd.

This paper investigates the performance of a new adaptive call admission policy based on a window-measurement estimation of the status of the buffer at the base station where two classes of customers (voice and data) are multiplexed into L servers. In this work, we interrelate physical limitations of the base stations (i.e., the number of transmission and reception modems), call and burst level traffic, instantaneous ATM buffer conditions, and end-to end bit error performance. Considering these parameters, we combine them all in one queueing problem and devise a new hybrid congestion based call admission policy. This policy is investigated in conjunction with CDMA integrated networks utilizing the IS-95 transmission/reception platforms. The results show that the proposed policy does maintain the QoS required for the networks under expected traffic load.