Massive MIMO is expected to play an important role in the development of 5G networks. This paper addresses the issue of pilot contamination and scalability in massive MIMO systems. The current practice of reusing orthogonal pilot sequences in adjacent cells leads to difficulty in differentiating incoming inter- and intra-cell pilot sequences. One possible solution is to increase the number of orthogonal pilot sequences, which results in dedicating more space of coherence block to pilot transmission than data transmission. This, in turn, also hinders the scalability of massive MIMO systems, particularly in accommodating a large number of IoT devices within a cell. To overcome these challenges, this paper devises an innovative pilot allocation scheme based on the data transfer patterns of IoT devices. The scheme assigns orthogonal pilot sequences to clusters of devices instead of individual devices, allowing multiple devices to utilize the same pilot for periodically transmitting data. Moreover, we formulate the pilot assignment problem as a graph coloring problem and use the max k-cut graph partitioning approach to overcome the pilot contamination in a multicell massive MIMO system. The proposed scheme significantly improves the spectral efficiency and enables the scalability of massive MIMO systems; for instance, by using ten orthogonal pilot sequences, we are able to accommodate 200 devices with only a 12.5% omission rate.

Two models are proposed to optimize unmanned aerial vehicles (UAVs) traffic offloading in cellular networks. The first model optimizes the realtime traffic service, while delaying non-realtime traffic in the cell buffers (BS and the currently serving drone). Delayed traffic is then transmitted later when free resources are available and has a maximum service delay limit. The proposed model provides a heuristic solution to minimize the losses in non-realtime traffic based on the maximum delay and the size of the cell buffers. The second model completes the work by providing an optimal Integer Linear Programming solution to minimize the number of needed UAV assuming no data loss and buffering availability. It is also parameterized with the same delay limits as the first model. The performance of the proposed models is studied using the call detail record (CDR) dataset of the city of Milan cellular network provided by Telecom Italia. The two models showed great QoS performance based on the maximum delay of non-realtime traffic and cell buffer size compared to models that do not include the buffering and the delay limits.

The surge in the number of various types of connected devices with the upcoming 6G networks may surpass the capabilities of traditional wireless infrastructure. Specifically, unmanned aerial vehicles (UAV s) can serve users to enhance network coverage and capacity in regions with limited or no existing infrastructure. However, resource allocation constraints and complex dynamics of UAV s pose significant challenges in optimal operation, such as path planning. Recently, researchers proposed simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which may serve the multiple users residing in the transmission and reflection regions. Unfortunately, STAR-RIS is only capable of reflecting incident signals, thus restricting its potential to enhance service quality in intricate channel conditions, particularly when the node distance is substantial. Motivated by this, we introduce a novel concept named actively simultaneously transmitting and reflecting (ASTAR)- RISs, which can amplify incident signals in addition to reflection. When mounted on UAVs, they can provide an improved signal-to-noise ratio (SNR) to numerous users in remote or inaccessible areas. We formulate the ASTAR-RIS-UAV-assisted rate maximization problem subject to the UAV mobility constraint and aim to find the UAV optimal path planning for a given flight time. While the formulated problem is non-convex, successive convex approximation and iterative algorithms are used to find the optimal solution to the problem, followed by a heuristic approach. Simulation results show that the proposed model outperforms existing approaches regarding network performance and resource allocation, highlighting the potential of adding ASTAR-RISs in UAV-assisted wireless networks.

Due to the rapid increase of connected devices and network traffic, the data transport from end-user devices to destination (connected device, cloud, edge servers, etc) can be interrupted because of obstacles and problems. In this paper, we propose to integrate edge servers with the intelligent reflecting surface (IRS) in a vehicular edge computing (VEC) environment. The IRS is deployed in fixed places inside the city (fixed IRS-Edge Nodes) and in taxis and buses (mobile IRS-Edge Nodes), where it is used for both reflecting signals and executing the different client vehicles' tasks. We propose an Optimal IRS-Edge Selection (OIES) model to select the optimal IRS-Edge Node(s) that satisfy the client vehicles' requirements. Moreover, we propose an Efficient IRS-Edge Selection (EIES) algorithm to deal with the high number of client vehicles in dense networks. The numerical results demonstrate the efficiency and the feasibility of the proposed solution.

Reconfigurable intelligent surfaces (RISs) have emerged as an efficient and cost-effective technique to enhance a great variety of possible performances of the Internet of Things (IoT) systems by re-configuring the propagation environment. Motivated by this, we investigate the RIS-assisted edge computing systems for batteryless IoT sensors (b-IoT) under Rician fading channel conditions. We consider a fixed time frame divided into three slots. A b-IoT sensor harvests energy from radio frequency signals from a nearby base station (BS) during the first time slot. While performing local computation, the b-IoT sensor offloads computation bits to the BS and an IoT sensor using device-to-device communications protocol in the second and final time slots, respectively. An offloading ratio differentiates the fraction of computational bits, offloaded to BS and IoT sensors. We formulate the optimization problem with the convex sum of computational bits as objective function and energy consumption, offloading ratio, and energy harvesting constraints. We propose a gradient descent-based iterative algorithm to solve the optimization problem. Simulation assessments depict RIS panel-assisted edge computing, and energy harvesting enhances the performance by more than 90% compared to the traditional baseline schemes, such as networks with no RIS panel.

Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model.

The density increasing in Radio Access Networks (RAN) caused the migration of traditional base stations to the cloud to meet huge traffic of end-users' demands. In this context, virtualization techniques can add more flexibility and programmability to scale in/out virtual storage, network and computing resources. However, Cloud-RAN (C-RAN) requires real-time processing and scheduling of its demands represented as service chains. In this paper, we formulate the joint assignment and scheduling problem in C-RAN using linear programming approach. Placement and scheduling algorithms allowing to allocate efficiently computing resources for C-RAN Virtual Network Functions (VNFs) with respect to the RAN services chaining are introduced and their behavior is quantified through real traces. We illustrate and highlight the feasibility and efficiency of our proposed algorithms through different scenarios in various considered network instances. Metrics such as cpu cores occupancy, network throughput, and successful subframe decoding rate are used to illustrate our algorithms' efficiency.

Due to the dense deployment of a small base station (SBS), wired backhauling is not always available, nor it is efficient. Therefore mmWaves are introduced to serve as backhauling links that offer high backhauling throughput and low CAPEX. However, mmWaves suffer from a high attenuation rate as the distance between SBSs and a macro base station (MBS) increases, which can severely degrade the system performance. Therefore, it is more efficient to use some SBSs to aggregate from different SBSs to MBS. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. In this work, we present a new network model in which SBSs are able to harvest energy from a renewable source and utilize it for backhauling and their associate UEs. A mathematical Optimization problem is formulated to solve UEs association, dynamic sleeping, backhauling, and transmission power. Moreover, due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, a heuristic backhauling and dynamic sleeping (HBDS) algorithm is introduced to decomposes the formulated problem into two parts and solve it iteratively. Finally, computer simulation results that demonstrate the model’s performance are presented for comparison between optimal solution and HBDS, which shows that HBDS has better computation efficiency with minimum performance difference.

Recently, there has been a flurry of research on the use of Reconfigurable Intelligent Surfaces (RIS) in wireless networks to create dynamic radio environments. In this paper, we investigate the use of an RIS panel to improve bi-directional communications. Assuming that the RIS will be located on the facade of a building, we propose to connect it to a solar panel that harvests energy to be used to power the RIS panel’s smart controller and reflecting elements. Therefore, we present a novel framework to optimally decide the transmit power of each user and the number of elements that will be used to reflect the signal of any two communicating pair in the system (user-user or base station-user). An optimization problem is formulated to jointly minimize a scalarized function of the energy of the communicating pair and the RIS panel and to find the optimal number of reflecting elements used by each user. Although the formulated problem is a mixed-integer nonlinear problem, the optimal solution is found by linearizing the non-linear constraints. Besides, a more efficient close to the optimal solution is found using Bender decomposition. Simulation results show that the proposed model is capable of delivering the minimum rate of each user even if line-of-sight communication is not achievable.

The emergence of new vehicles generation such as connected and autonomous vehicles led to new challenges in the vehicular networking and computing managements to provide efficient services and guarantee the quality of service. The edge computing facility allows the decentralization of processing from the cloud to the edge of the network. In this paper, we design and propose an end-to-end, reliable and low latency communication architecture that allows the allocation of compute-intensive autonomous driving services, in particular autopilot, to shared resources on edge computing servers and improve the level of performance for autonomous vehicles. The reference architecture is used to design an Advanced Autonomous Driving (AAD) communication protocol between autonomous vehicles, edge computing servers, and the centralized cloud. Then, a mathematical programming approach using Integer Linear Programming (ILP) is formulated to model the autopilot chain resources Offloading at the network edge. Further, a deep reinforcement learning (DRL) approach is proposed to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Moreover, several scenarios are considered to quantify the behavior of the optimization approaches. We compare their efficiency in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated Edge Autopilots.

Operating at lower frequencies than systems such as Wi-Fi, TVWS wireless communication can enable long-range communication in rural communities and can more easily penetrate obstacles (vegetation, terrains). Thus, it is appealing to scenarios where line-of-sight is not always guaranteed. In particular, TVWS communication is a good candidate for supporting precision agriculture such as camera-based plant phenotyping and sensor-based analysis of plant behaviour. Yet there lacks in-depth real-world measurement data on the behavior of TVWS wireless channels in agriculture farms. To fill this gap, we use the field-deployed TVWS network of CyNet to measure TVWS channel behaviour in the Curtiss Research Farm in Ames, Iowa, where the landscape is predominantly composed of soybean and corn fields. We investigate the impact that crop diversity (soybean vs. corn), height and density of corn fields, antennas’ placement and variations of temperature and humidity have on the spatiotemporal behaviour of TVWS channels. This study also helps identify path loss models that best reflect radio propagation characteristics of TVWS systems in corn farms for different antenna heights.

Radio access network (RAN) virtualization is gaining more and more ground and expected to re-architect the next-generation cellular networks. Existing RAN virtualization studies and solutions have mostly focused on sharing communication capacity and tend to require the use of the same PHY and MAC layers across network slices. This approach has not considered the scenarios where different slices require different PHY and MAC layers, for instance, for radically different services and for whole-stack research in wireless living labs where novel PHY and MAC layers need to be deployed concurrently with existing ones on the same physical infrastructure. To enable whole-stack slicing where different PHY and MAC layers may be deployed in different slices, we develop PV-RAN, the first open-source virtual RAN platform that enables the sharing of the same SDR physical resources across multiple slices. Through API Remoting, PV-RAN enables running paravirtualized instances of OpenAirInterface (OAI) at different slices without requiring modifying OAI source code. PV-RAN effectively leverages the inter-domain communication mechanisms of Xen to transport time-sensitive I/Q samples via shared memory, making the virtualization overhead in communication almost negligible. We conduct detailed performance benchmarking of PV-RAN and demonstrate its low overhead and high efficiency. We also integrate PV-RAN with the CyNet wireless living lab for smart agriculture and transportation.

This paper presents a framework based on the data analysis concept to automate the management of resources in cellular networks. Three processes are defined: identifying and detecting anomalies, analyzing the causes, and triggering adequate recovery actions. First, the proposed solution executes Deep Learning algorithms to forecast the normal behavior of the network and defines dynamic thresholds. Then, it identifies cells with peak demands and raises alarms if the measured real-time data exceeds the threshold values. Second, we define QoS optimization methods to proceed with suitable design for resource allocation as well as fault detection and avoidance. Hence, we distinguish three cases and define two classes of data: Real-time and non-real-time traffic. This solution is applied to a pre-analyzed semi-synthetic real dataset extracted from the CDRs (Call Detail Records) in Milan city, Italy. This dataset contains the Internet activity records of two months in three areas. The preliminary results elucidate the feasibility and preeminence of our proposed anomaly detection framework.

In this paper an optimization problem to minimize the energy drawn from the network grid by utilizing the harvested energy and dynamic sleeping of the Small Base Stations (SBSs) is presented. Due to the complexity of the optimization problem, a new UEs' movement prediction method is introduced to provide future information for the model to apply an accurate optimization problem. This method is based on a combined approach of Non-linear Autoregressive with External input (NARX) and probabilistic Latent Semantic Analysis (pLSA) to provide accurate prediction for multiple steps. Furthermore, extensive simulation results are presented to show the effectiveness of our approaches in comparison to the optimal results.

The emerging of the internet of things (IoT) led to increasing the computation resources required to satisfy a large number of requests from the connected devices, for this the Cloud Computing (CC) allows the processing of requests in the cloud to guarantee the efficiency of services for end-users. The main problem of the current CC architecture is the latency in real-time applications such as video streaming, which require a distributed architecture to support the future generation of applications. The Mobile Edge Computing (MEC) provides a fully distributed architecture where a part of processing executed in the edge of network which supports the requirements of IoT applications. In this paper, we propose to use the connected devices as on-demand virtual edge servers to provide computation services close to endusers where each submitted task is divided into a set of sub-tasks, each one can be executed by any other device which is a part of the virtual edge server according to the available resources in the selected device. In this context, we have formulated the partitioned and the offloading problem in MEC environment using linear programming techniques. Optimal Partitioned and Offloading (OPO) algorithm that allocates network, storage and computing resources to user application sub-tasks with respect to MEC constraints and user quality requirements is modeled, implemented, and evaluated. Results show the feasibility and efficiency of the proposed algorithms.

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.

Energy harvesting communications systems are able to provide high quality communications services using green energy sources. This paper presents an autonomous energy harvesting communications system that is able to adapt to any environment, and optimize its behavior with experience to maximize the valuable received data. The considered system is a point-to-point energy harvesting communications system consisting of a source and a destination, and working in an unknown and uncertain environment. The source is an energy harvesting node capable of harvesting solar energy and storing it in a finite capacity battery. Energy can be harvested, stored, and used from continuous ranges of energy values. Channel gains can take any value within a continuous range. Since exact information about future channel gains and harvested energy is unavailable, an architecture based on actor-critic reinforcement learning is proposed to learn a close-to-optimal transmission power allocation policy. The actor uses a stochastic parameterized policy to select actions at states stochastically. The policy is modeled by a normal distribution with a parameterized mean and standard deviation. The actor uses policy gradient to optimize the policy’s parameters. The critic uses a three layer neural network to approximate the action-value function, and to evaluate the optimized policy. Simulation results evaluate the proposed architecture for actor-critic learning, and shows its ability to improve its performance with experience.

Internet of Things (IoT) is a promising technology that enables interconnecting billions of electronic devices over communication networks. Multicasting is an essential service in IoT which allows the IoT devices to disseminate common messages more efficiently. Energy consumption is one of the main concerns in designing and implementing IoT since the IoT devices are expected to run for long periods of time using batteries in general. Moreover, IoT devices that participate in forwarding multicast messages excessively may deplete their energy sooner than expected. In this paper, we proposed to employ Radio Frequency (RF) energy harvesting technology with the IoT device in order to wirelessly power multicast sessions. Each IoT device that forwards a multicast message is compensated for the energy consumed for transmission by energy transmitted from Energy Transmitters (ETs). We formulate an optimal operational strategy, where the objective is to minimize the total transmitted energy from the ETs. The problem is in the form of non-convex Mixed Integer Nonlinear Problem, where there is no efficient way to solve the problem optimally when the number of variables is relatively large. Therefore, we first approximate the data rate function with a concave lower bound function. Then, we decompose the optimization problem using Generalized Bender Decomposition (GBD) into: 1) Convex Nonlinear Program (NLP) and 2) Mixed Integer Linear Program (MILP). Moreover, we employ Successive Convex Programming (SCP) within GBD algorithm to iteratively find a better approximation for the original problem. Our simula- tion results show that GBD-SCP algorithm solves the optimization problem more efficiently with a performance close to optimal.

Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs' energy more efficiently.

Information-Centric Networking is considered as one of the most promising architecture for IoT. The use of content-centric approach may improve the content access & dissemination, reduce the content retrieval latency, and enhance the network performance. The use of in-network caching in ICN enhances the data availability in the network, overcomes the issue of single-point failure, and improves IoT devices power efficiency. In this paper, we present a Near-ICN Cache Placement (NCP) scheme for IoT taking traffic class into consideration. NCP is designed to select the optimal replica cache by minimizing: the cost of moving the data from content producer to replica nodes, the cost of caching the content in the replica and the cost of delivery the content to consumers. Hence, we presented a multi-objective optimization problem, with a heuristic caching selection algorithm. We evaluated NCP with various performance metrics against different caching schemes. The obtained results show improvement in the cache utilization, with fast data retrieval, and enhancement in the network cache distribution & diversity.

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or longterm. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs), An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users' Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizina the UAVs' consumed energy.

This paper considers a heterogenous network HetNet where a macro base station (MBS) coexists with many small base stations (SBSs). SBSs can be deactivated and put to sleep to save energy and are equipped with two power sources, harvested energy (HE) source and a power grid source, where first the SBS will use its available HE to serve the associated users. Then, the SBS will request any shortage of its energy from other active or deactivated SBSs which have surplus of HE. Finally, if there is still shortage in energy, the SBS will use the power drawn from the grid. This transfer of energy is facilitated through the use of the promising technology of the smart grid (SG). We investigate the grid energy minimization problem by optimizing both the transmission power and activation/deactivation (Dynamic Sleeping) of the SBSs. Moreover, a decomposition of the problem into a convex optimization problem and users association according to the best SINR is proposed. Then, we derived a closed form of the optimal transmission power and use the IPOPT algorithm to find the Lagrangian variables. The results clearly show the advantages of our model operational strategy.

This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs.

In this paper, we investigate the problem of optimal planning and deployment of multiple relays to support energy-efficient uplink transmissions of Internet of Things (IoT) devices. A novel approach is proposed to optimize the relay locations with the objective of minimizing the total energy consumption of the network. In addition, the uplink transmit power of IoT devices and the device-relay-channel association are jointly optimized to meet the QoS requirement of IoT devices. A mixed-integer linear programming (MILP) problem is formulated to obtain the optimal solution. We also design a low-complexity genetic algorithm to provide a sub-optimal solution to the problem.

Prolonging the lifetime, and maximizing the throughput are important factors in designing an efficient communications system, especially for energy harvesting-based systems. In this work, the problem of maximizing the throughput of point-to- point energy harvesting communications system, while prolonging its lifetime is investigated. This work considers more real communications system, where this system does not have a priori knowledge about the environment. This system consists of a transmitter and receiver. The transmitter is equipped with an infinite buffer to store data, and energy harvesting capability to harvest renewable energy and store it in a finite battery. The problem of finding an efficient power allocation policy is formulated as a reinforcement learning problem. Two different exploration algorithms are used, which are the convergence- based and the epsilon-greedy algorithms. The first algorithm uses the action-value function convergence error and the exploration time threshold to balance between exploration and exploitation. On the other hand, the second algorithm tries to achieve balancing through the exploration probability (i.e. epsilon). Simulation results show that the convergence-based algorithm outperforms the epsilon-greedy algorithm. Then, the effects of the parameters of each algorithm are investigated.

Unmanned Aerial Vehicles (UAVs) enabled communications is a novel and attractive area of research in cellular communications. It provides several degrees of freedom in time, space and it can be used for multiple purposes. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. In this paper, we present a novel cell outage compensation (COC) framework to mitigate the effect of the failure of any outdoor Base Station (BS) in 5G networks. Within our framework, the outage compensation is done with the assistance of sky BSs (UAVs) and Ground BSs (GBSs). An optimization problem is formulated to jointly minimize the energy of the Drone BSs (DBSs) and GBSs involved in the healing process which accordingly will minimize the number of DBSs and determine their optimal 2D positions. In addition, the DBSs will mainly heal the users that the GBS cannot heal due to capacity issues. Simulation results show that the proposed hybrid approach outperforms the conventional COC approach. Moreover, all users receive the minimum quality of service in addition to minimizing the UAVs' consumed energy.

In this paper, we study the problem of energy minimization for multicasting in cognitive radio networks, using omnidirectional and directional antennas. Our objectives are to build the multicast routing tree and schedule the secondary users' transmissions such that the total energy consumption is minimized. We consider in this paper the energy consumption due to channels switching, and we study the channel switching and scheduling dependency. We model the problem as a MultiLayer Hyper Graph, then we formulate the optimization problem as Mixed Integer Linear Program (MILP). Moreover, we propose a heuristic algorithm to solve our problem in polynomial time. We also show how the primary users, directional antennas, the number of available channels and channel switching time per frequency jumping step affect the total energy consumption. The simulation results shows that our proposed algorithm achieves close performance to the optimal solution.

The Information-Centric Network (ICN) is very promising in the area of Complex Active Networks (CANs), where the data-centric approach is useful in reducing the data retrieval latency as well as the network traffic of active networking services. Also, the in-network caching and processing capabilities in ICN limits the massive data access to the data producers and so relaxes the need of continuous E2E connectivity between data producers and data consumers. In this paper, we present an ICN-based architecture in which ICN nodes provide processing/treatment capabilities and caching functions. Service placement algorithms (OPPA and HPPA) in CANs are proposed to choose optimal and near-optimal placement location for ICN nodes. We evaluated the algorithms with respect to several performance metrics and the obtained results show improvement in services consumption latency and network load. Furthermore, we propose a caching strategy that shows to stabilize the network load despite any increase in the number of consumer interests.

In this paper, we consider a mobile cognitive base station (MB) to transfer data and energy to IoT devices. The MB receives requests for data and energy from the cluster heads of the IoT devices, then it adjusts its transmission power and location to transfer data and energy to the IoT devices. The goal is to support data and energy demands of IoT devices within a certain time to guarantee a certain quality of service while minimizing the total energy consumption. We consider the energy consumption due to the mobility of the MB in addition to the energy consumed for data and energy consumption. The MB operates underlaying a primary network, and hence, the MB adjusts its transmission power and location such that the primary users are protected from harmful interference. We developed a mathematical model to solve the problem and find the optimal locations and transmission powers that minimize the total energy consumption. Simulation results show how the IoT devices, the primary users, the number of the MB locations, service time and the mobility of the MB influence the total energy consumption. We show that increasing the number of the MB locations and the maximum tolerable time for delivering data and energy allow the MB to provision the service while reducing the total energy consumption.

In this paper, we investigate energy efficient and energy harvesting (EH) in heterogeneous networks (HetNets) where all base stations (BSs) are equipped to harvest energy from renewable energy sources, e.g., solar. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSs can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the network-wide energy consumption subject to users' certain quality of service and BSs' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. Two cases based on the knowledge level about future renewable energy (RE) statistics are investigated: (i) an online knowledge case where future RE statistics are unknown, (ii) an offline knowledge case where future network's statistics are a priori perfectly estimated. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.

Cognitive radio networks with energy harvesting result in efficient use of both energy and spectrum. By using cooperative relaying, another feature can be achieved, which is the high diversity gain. In this paper, an energy harvesting underlay cognitive radio relaying network is investigated. In this underlay cognitive radio scheme, secondary users are allowed to access the spectrum, respecting a certain primary interference threshold. The secondary nodes employ decode-and-forward relaying in order to maximize the total received data by optimizing their transmit powers. In this context, both the secondary source and relay harvest energy from renewable sources and store it in finite batteries. They are also capable of buffering data in infinite capacity buffers. We derive closed form expressions for transmit power of secondary source and relay that maximize the secondary network throughput. Projected subgradient method is used to find the power allocated to the secondary network. Numerical simulations are conducted to study the performance of the proposed system. Comparisons are made between the proposed system and other conventional scenarios, and it is observed that when the required signal-to interference-plus-noise ratio (SINR) at the primary receiver is high, the proposed harvesting- based scheme and conventional-based scheme perform similarly.

Cooperation among network devices is a promising solution to improve network throughput and network service quality. In addition, it can be used to enhance network survivability against failures. In this paper, we study the user cooperation solution of multipath streaming application on wireless user equipments (UEs) using auction theory. We assume that UEs use multipath transport layer service, and establish two paths for streaming events, one path goes through its cellular link, another path is established using a Wi-Fi connection with a neighbor UE. We propose two user cooperation schemes (LCF and EAC) for UEs to participate in the user cooperation system. We compare the performance of using LCF and EAC schemes to the scheme without cooperation, and the simulation results show that applying the proposed user cooperation scheme and establishing multipath connections for the streaming event can reduce up to 60% of total energy consumed. LCF scheme can also help balance the energy consumption among UEs in the system.

Network resource virtualization emerged as the future of communication technology recently, and the advent of Software Define Network (SDN) and Network Function Virtualization (NFV) enables the realization of network resource virtualization. NFV virtualizes traditional physical middle-boxes that implement specific network functions. Since multiple network functions can be virtualized in a single server or data center, the network operator can save Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) through NFV. Since each customer demands different types of VNFs with various applications, the service requirements are different for all VNFs. Therefore, allocating multiple Virtual Network Functions(VNFs) to limited network resource requires efficient resource allocation. We propose an efficient resource allocation strategy of VNFs in a single server by employing mixed queuing network model while minimizing the customers' waiting time in the system. The problem is formulated as a convex problem. However, this problem is impossible to be solved because of the closed queuing network calculation. So we use an approximation algorithm to solve this problem. Numerical results of this model show performance metrics of mixed queuing network. Also, we could find that the approximate algorithm has a close optimal solution by comparing them with neighbor solutions.

In this paper, a multiple relay selection scheme for Energy Harvesting (EH)-based two-way relaying is investigated. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, then use it to forward the information to the sources. The time-switching protocol (TS), in which the receiver switches between transmitted information and harvested energy, is adopted in the relay side. The goal is to find the optimal TS ratios associated with the selected relays that maximize a rate-based utility function over multiple coherent time slots. Two metrics reflecting the degrees of fairness in the optimization are investigated. A joint-optimization solution based on binary particle swarm optimization is proposed to solve the problem. Numerical results illustrate the behavior of the TWR network according to the considered utility functions, the generated amount renewable energy, in addition to other system parameters.

Virtual Content Delivery Network (vCDN) migration is necessary to optimize the use of resources and improve the performance of the overall SDN/NFV-based CDN function in terms of network operator cost reduction and high streaming quality. It requires intelligent and enticed joint SDN/NFV migration algorithms due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, two approaches for finding the optimal and near optimal path placement(s) and vCDN migration(s) are proposed (OPAC and HPAC). Moreover, several scenarios are considered to quantify the OPAC and HPAC behaviors and to compare their efficiency in terms of migration cost, migration time, vCDN replication number, and other cost factors. Then, they are implemented and evaluated under different network scales. Finally, the proposed algorithms are integrated in an SDN/NFV framework.

In this paper, we investigate the Energy Harvesting (EH)-based two-way relaying system using Amplify-and-Forward (AF) and Decode-and-Forward (DF) strategies. The relay is considered as an EH node that harvests the received Radio Frequency (RF) signal and uses this harvested energy to forward the information. Two relaying protocols based on Time Switching (TS) and Power Splitting (PS) receiver architectures are proposed to enable EH and information processing at the relay. Analytical throughput expressions are derived and optimized for both protocols. The goal is to find the optimal TS and PS ratios that maximize the total throughput Numerical results illustrate the performance of TS and PS protocols for different strategies, and show that at high signal-to-noise ratio, PS is superior to TS, and AF is superior to DF in terms of achievable sum-rate.

In this paper, we investigate the problem of routing for supporting many-to-many communication and optimizing the Quality of Service (QoS) for the Secondary Users (SUs) in Cognitive Radio Networks (CRN). The goal is to build the routing tree for many-to-many communication that optimizes the delay, rate and the number of transmission links. We model the network using the Multi-Layer Hyper Graph, and we model the optimization problem as an Integer Linear Program (ILP). We also study the problem of many-to-many communication scheduling in CRNs. We employ a modified version of an Ant Colony optimization algorithm (MOACS) to solve our problem of finding the routing tree that optimizes our objectives. Our simulation results show that the performance of MOACS outperforms the Shortest-Path Tree (SPT) in all our objectives.

In this paper, the problem of radio and power resource management in underlay cognitive radio heterogeneous networks is investigated, where macro and pico Base Stations (BSs) are considered as primary BSs while femto BSs are considered as secondary BSs. The goal is to minimize the total primary power consumption and maximize the secondary utility of the network while satisfying the primary user quality of service determined by target data rate and interference constraints. Furthermore, a green communication algorithm is implemented based on a sleeping strategy. Simulations study investigates the performance of the proposed scheme and shows an important saving in terms of total power consumption.

Self-healing mechanism is one of the three functionalities for self-organizing networks, and it has three major components to be studied by the academic society: fault detection, fault diagnosis and cell outage compensation. In this paper, we study the cell outage compensation function of the self-healing mechanism. In a heterogeneous network environment with densely deployed Femto Base Stations (FBSs), we form a resource allocation problem for FBSs and Femto User Equipments (FUEs) operations using Coordinated Multi-Point (CoMP) transmission and reception with joint processing technique. Since the formulated problem is considered as NP hard problem, we propose a heuristic operation scheme to solve the problem. Simulation results show that our proposed operation scheme can improve FUE throughput by up to 30% compared to other solutions, and it can also prevent the system total rate loss from having the same speed of radio resource loss when failures happen.

The problem of energy consumption in data centers has attracted many researchers interest recently. This paper proposes an optimal energy consumption Software Defined Network (SDN) data center model using the dynamic activation of hosts and switches. We model switches and hosts as queues and formulate a Mixed Integer Linear Programming (MILP) model to minimize energy consumption while guaranteeing Quality of Service (QoS) of data center. Our purpose is minimizing static power, port power, and memory power of data centers. Since the problem is NP-hard, we adopt Simulated Annealing algorithm to obtain the solution. Through numerical experiment, we could observe that our model is able to save reasonable energy compared to the full operation data center model.

Due to the increasing bandwidth demand of mobile users and their devices with energy hungry wireless networking modules, attention of research efforts has been recently shifting to find answers to the paradox of achieving more spectrum for less energy consumption. In this paper, cognitive radios have been employed to obtain more spectrum by utilizing unused licensed spectrum in an opportunistic manner. Defining the opportunity cost as the consumed energy per achieved unit of free spectrum, we propose a cooperative sensing scheduling framework to optimize the cost with the consideration of the sensing, reporting and channel switching costs in terms of energy expenditure subject to a licensed user protection threshold. In the proposed scheme, all primary channels are scheduled to be cooperatively sensed within a cycle which consists of rounds. In every round, secondary/unlicensed users (SUs) are first assigned to cooperatively sense the scheduled primary/licensed user (PU) channels. Consequently, SUs report their local sensing results to a fusion center for a global decision. Finally, SUs assigned to sense other PU channels perform channel switching for the next round. This scheme not only provides a feasible network set up in case there does not exist a sufficient number of SUs to satisfy the PU protection in a single round, but also offers an apparent reduction in the opportunity cost.

4G/5G Heterogeneous Networks (HetNets), which are expected to have a very dense multi-layer network structure, have emerged as a solution to satisfy the increasing demand for high data rates. These networks, similar to other networks, are subject to failures of communication components, which may occur due to many reasons. Self-Healing (SH) is the ability of the network to continue its normal operation in the presence of failures. The contribution of this paper is to introduce a novel SH approach for all network base-stations (BSs) back-hauling in a HetNet. New SH radios are proposed with enabled Cognitive Radio (CR) capabilities for utilizing the spectrum. A Software Defined Wireless Network Controller (SDWNC) is used to handle all control information between all network elements (except user equipment). This novel pre-planned reactive SH approach ensures network reliability under multiple failures. A simulation study is conducted to assess the performance of our approach through the evaluation of the Degree of Recovery (DoR) under single and multiple failures. Our approach can achieve a DoR of at least 10% using only 1 SHR and an enhanced DoR can be achieved using a greater number of SHRs.

In this paper, we study the channel allocation problem in cognitive radio wireless mesh networks (CR-WMNs). We aim at finding an allocation strategy that guarantees quality of service (QoS) (link reliability), maximizes network coverage, and alleviates the need for a common control channel to coordinate the communication process. The allocation of a particular channel to a mesh client (MC) is considered feasible if the MC can establish connectivity with the backbone network in both the upstream and the downstream directions, and has the signal-to-interference-plus-noise ratio (SINR) of the uplink and the downlink with its parent mesh router (MR) within a predetermined threshold. A receiver-based channel allocation (RBA) model that achieves the aforementioned objectives is proposed (channel assignment under this model can be proven to be NP-hard). We then formulate a mixed integer linear program, of the channel allocation problem under the proposed model, and compare its performance to that of two other baseline models, namely, transmitter-based and all-tunable channel allocation strategies. The results prove the superiority of the proposed model. We also developed a heuristic algorithm, which is shown to be an accurate algorithm.

Cooperative spectrum sensing (CSS) has been extensively studied in the literature to mitigate the weakness of spectrum sensing against hostile propagation phenomenon. Especially for large networks, clustered CSS is preferred to alleviate the energy efficiency, delay and overhead problems. In this study, reporting and sensing channels are first modeled with the consideration of path loss and fading. Then, CSS is divided into three phases: 1) In sensing phase, optimal sensing time is obtained for each local user subject to local detection and false alarm probability thresholds, 2) In reporting phase, adopting Dijkstra's algorithm, multi-hop paths with the maximum success rate and cluster head (CH) selection which gives the mimimum total error rate within each cluster is computed, and 3) In decision phase, collecting independent but unidentically distributed (i.u.d.) member decisions, the CH decides on channel occupancy based on an optimal voting rule for i.u.d. reports. Next, following the phases above, a multi-objective clustering optimization (MOCO) is formulated to select SUs into cluster seeking energy and throughput efficiency goals subject to global detection and false alarm probability constraints. Finally, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to solve MOCO. Results based on our approach are presented and the merits of this approach are demonstrated.

Today's wireless networks use fixed spectrum over long term and fixed geographical regions. However, spectrum utilization varies by time and location, which leads to temporal and special spectrum underutilization. Cognitive radio is an emerging technology that enables dynamic sharing of the spectrum in order to overcome the spectrum underutilization problem. In this paper, we consider the problem of supporting the multicast service mode in cognitive radio networks. Moreover, we are concerned with supporting this mode of service such that it is robust in the face of failures. We develop two algorithms which provide robust multicasting in such networks. Our proposed algorithms are: 1) multicast sessions protection with link-sharing trees and 2) multicast sessions protection using rings. These algorithms provision multiple multicast sessions, and protect them against a single primary user interruption at a time. They also take into account that the activities of a primary user may disrupt communication in several groups, of secondary users, which are referred to as Shared Primary User Risk Group (SPURG). The objective of the proposed algorithms is to increase the number of sessions that can be accommodated in the network and minimize the cost of provisioning the sessions while protecting them against failures.

Fifth generation mobile systems (5G) target an Average Area Spectral Efficiency (AASE) over hundred Gbps/km2/user for future mobile systems with an Energy Dissipation (ED) per unit area similar to the current ED levels. Heterogeneous networks (HetNets) with high density of deployed small cells are currently adopted to aid in achieving the target ED and AASE by 5G. Limited spectrum availability requires efforts to manage the spectrum utilization in such dense deployments. Development of new network architectures and Radio Resources Management (RRM) schemes is important to address such challenges. The objective of this work is to propose a new architecture that consists of a Decision Support System (DSS) and a data collection system to dynamically manage and control the spectrum allocation process. The DSS generates spectrum allocation patterns using non-parametric estimation and statistical analysis for the collected data. A new RRM model using a Plan, Do, Control and Act (PDCA) cycle is proposed as a new self optimization module in the self organizing network framework. The PDCA model utilizes the new architecture and the allocation patterns to dynamically predict future spectrum allocation. Results show improvement in the AASE achieved using the PDCA model compared to conventional spectrum allocation.

In this paper, we study the multicast routing problem in Cognitive Radio Networks (CRNs). We propose a new network modeling method, where we model CRNs using a Multi-rate Multilayer Hyper-Graph (MMHG). Given a multicast session of the MMHG, our goal is to find the multicast routing trees that minimize the worst case end-to-end delay (delay), maximize the multicast rate (rate) and minimize the number of transmission links (numOfLinks) used in the multicast tree. We apply two metaheuristic algorithms (Multi-Objective Ant Colony System optimization algorithm (MOACS) [1] and A Simulated Annealing-Based Multi-objective Optimization Algorithm (AMOSA) [2]) in solving the problem. We also study the scheduling problem of multicast routing trees obtained using the MMHG model. Our simulation results show that within a few seconds, MOACS can find more than 60% of the approximated Pareto Front (APF) in small CRNs, and AMOSA can find approximately 45%. Moreover, the solutions found by MOACS and AMOSA that are not in the APF are within 10% relative distances to solutions in the APF.

Cognitive radio networks (CRNs) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. A well-known property of CRNs is the potential heterogeneity in channel availability among secondary users. Therefore, multicast throughput in CRNs may suffer from significant degradation because of this property since a link-level broadcast of a frame may only reach a small subset of destinations that are able to receive on the same channel. This may necessitate multiple sequential transmissions of the same frame by the source on different channels to guarantee delivery to all receivers in the destination set. In case of high data generation rate, delivery delay will be high due to the repeated transmissions by the source. In this paper, we propose an assistance strategy to reduce the effect of the channel heterogeneity property on the multicast throughput in cognitive radio wireless mesh networks (CR-WMNs). This assistance strategy is composed of two main activities: first, allowing multicast receivers to assist the source in delivering the data, and second, allowing the transmission of coded packets so that multicast receivers belonging to different multicast groups can decode and extract their data concurrently. Results show that the proposed assistance paradigm reduces multicast time and increases throughput significantly.

Routing in cognitive radio networks (CRNs) necessitates a cross-layering approach. However, according to [1], CRN routing protocols proposed in literature are partially cross-layer, because the information flow is only from physical layer to network layer, e.g., about channels availabilities. In this work, we introduce a cross-layer routing protocol (CLRP), which considers both the channels that are known to be available at each node, as well as other channels that may be available. The availabilities of the latter channels are considered using a stochastic approach. CLRP computes an end to end path, and feeds the physical layer with information about which channels to sense and which nodes should perform the sensing, such that the expected route quality is enhanced. Simulation results show that CLRP outperforms other cross-layer routing protocols in terms of throughput and stability of the path being setup, and increases the probability of finding an end-to-end path.

Cognitive Radio Networks (CRNs) have a dynamic nature where channels availability changes over time. In this paper, we introduce a strategy to route multicast sessions in CRNs and to protect them against failures or disappearance of channels. We model the network as a Multilayer Hyper-Graph (MLHG), such that a group of Secondary Users (SUs) which have a common channel are modeled by a hyper-edge. Also, each layer in the MLHG represents a different channel. Primary paths from a source SU to destination SUs are selected by considering channels' switching delay, and transmission delay. To protect the multicast session, we select a backup path for primary path, if feasible, such that the primary and backup paths are Shared Risk Hyper-edge Groups (SRHEGs) disjoint. We develop an Integer Linear programming (ILP) model, in order to find the multicast primary paths and their backup paths, minimize the maximum path delay, and minimize the number of selected channel links. Our simulation results show that when the number of available channels increases, the number of primary and backup paths that can be routed in the CRN increases, and the maximum path delay decreases almost linearly.

In this paper, we present a new technique that uses deterministic binary network coding in a distributed manner to enhance the resiliency of sensor-to-base information flow against packet loss. First, we show how to use network coding to tolerate a single packet loss by combining the data units from k sensor nodes to produce k+1 combinations such that any k of them are solvable. After that, we extend the solution to tolerate multiple losses. Moreover, we study the coding efficiency issue and introduce the idea of relative indexing to reduce the coding coefficients overhead. To tolerate node or link failures, we introduce a simple routing protocol that can find maximally disjoint paths from the k sensor nodes to the base station (BS). We study the relationship between the probability of successful recovery of all data units at the BS, and the number of sources protected together taking into consideration their hop distance from the BS. From this study, we can decide on the appropriate number of sources to be protected together, so that the probability of successful recovery is higher than a certain threshold. Finally, we show through a simulation study that our approach is highly scalable and performs better as the network size increases.

The goal of this paper is to model heterogeneous channel Access in Cognitive Radio Networks (CRNs). In CRNs, when licensed users, known as Primary Users (PUs), are idle, unlicensed users, known as Secondary Users (SUs) can use their assigned channels. In the model we consider in this paper, there are two types of licensed channels, where one type has a larger bandwidth, and hence a higher service rate for SUs. Therefore, SUs prefer to use such channels, if available, over channels in the second type which have a lower service rate. SUs may also switch from the second to the first type of channels when they become available, even if their current channels are still available. Moreover in our performance model, we model the SUs' sensing process, and its dependence on the system load, and number of sensing users. We use a Continuous Time Markov Chain (CTMC) modeling approach, and derive SUs' performance metrics, which include SUs admission and blocking probabilities, and their average waiting time in the system. We also develop a baseline model and compare its performance to our proposed model.

To properly operate closed industrial control networks, it is required that communication with nearly constant delay bounds be supported. In [24] the authors introduced FlexTDMA in order to provide this support, and with minimal delay-jitter in an asynchronous network, under unicast communication. In this paper we consider providing this support for simultaneous multicasting, and introduce the FlexTDMA++ protocol. Under this protocol, and with periodic on-off traffic that is directed to multiple receivers, frame losses and switch failures are managed in the presence of component clock drifts and bandwidth loads.

In this paper, we consider the problem of providing fault tolerant operation for multicast networks with multiple sources, e.g., sensors, and delivering data to a pre-defined set of destinations, e.g., sinks. We propose an algorithm that combines network and erasure coding to provide resilience against a predefined number of link failures. For sources unable to meet the flow constraints, support is provided at the cost of reduced throughput and if required, reduced protection. The necessary and sufficient flow conditions for network resource verification are proved. We also prove that the field size for coding coefficients is bounded by the amount of protection offered for erasure coding and equal to 2 for network coding.

In this paper, we propose to recover collided packets between Primary Users (PUs) and Secondary Users (SUs) in Cognitive Radio Networks (CRNs) for two scenarios. When a collision occurs between an SU and a PU transmitters, the SU's receiver considers the PU's transmitted packet's signals as an interference, and hence, cancels its effect in order to recover its corresponding received packet's signals. Recovering collided packets, instead of retransmitting them saves transmitters' energy. In the first scenario, we assume PUs and SUs employ the standard Binary Phase-Shift keying (BPSK) and a 90 degree phase shifted version, i.e., orthogonal to BPSK, respectively, as their modulation techniques. In the Second scenario, we assume PUs and SUs employ BPSK and QPSK as their modulation techniques, respectively, or vice versa. In both scenarios, we propose protocols to recover the SU collided packets, depending on the received phase shifts. We show through numerical analysis that a significant fraction of collided packets can be recovered. We also derive an energy saving performance metric for our proposed mechanisms, in order to assess the saved energy due to recovering the collided packets. Our numerical analysis also shows that a high percentage of energy can be saved over the traditional scheme, in which our packets recovery mechanisms are not employed.

To properly operate closed industrial control networks, it is required that communication with nearly constant delay bounds be supported. In [9] the authors introduced FlexTDMA in order to provide this support, and with minimal delay-jitter in an asynchronous network, under unicast communication. In this paper we consider how periodic on-off traffic and frame loss are managed in the presence of network component clock drifts and bandwidth loads, and introduce the FlexTDMA+ protocol. This protocol includes three improvements over FlexTDMA: baseline preemption, partial baselining and baseline deadline density control. In this paper we consider the relative value of each of these improvements, individually and in combination, has in handling these network conditions.

The cognitive radio (CR) nodes in a cognitive radio network (CRN) do not have license to use specific spectrum band. Instead, they use the spectrum bands of the licensed primary users (PU) without interfering with the PU. When the PU becomes active, interfering CRs should leave to another available spectrum band within the PU's tolerable interference delay (TID). Therefore, CRN operates over wide spectrum bands which span many channels. Since each channel is typically licensed to one PU, this requires that channels be sensed separately. This adds monitoring overhead, where the CR should monitor (sense) the channel every TID, which reduces the throughput. For this reason, the node cannot monitor the whole set of channels. Deciding which set of channels to monitor affect other functions in the CRN like routing. Work done on routing in literature assumes that each node maintains a set of available channels which is obtained by sensing. Route setup decision will be made based on the available sets at all nodes. However, there may be some other available channels that the node is not aware of their availability which may enhance the routing quality metric. Also, taking into consideration only the sets of channels available at the CR nodes may preclude finding an end-to-end path. In this paper, we propose a spectrum decision framework that is complementary to the existing routing protocols. This framework is based on two objectives: 1) enhancing the route quality by sensing a few more channels at some nodes. These channels can enhance the quality by: reducing the switching time, requiring shorter sensing time, or expected to be available for longer time; 2) increasing the probability of finding a path by sensing more channels at some nodes in case the routing protocol did not find a path. Simulation results show that the proposed framework can result in enhancement that can be as high as 100% over the routing protocols that build their decisions based on the available channels at each node only.

A large number of network applications today allow several users to interact together using the many-to-many service mode. In many-to-many communication, also referred to as group communication, a session consists of a group of users (we refer to them as members), where each member transmits its traffic to all other members in the same group. In this paper, we address the problem of grooming subwavelength many-to-many traffic (e.g., OC-3) into high-bandwidth wavelength channels (e.g., OC-192) in optical wavelength division multiplexing (WDM) mesh networks. The cost of an optical WDM network is dominated by the cost of higher-layer electronic ports (i.e., transceivers). A transceiver is needed for each initiation and termination of a lightpath. Therefore, our objective is to minimize the total number of lightpaths established. Unfortunately, the grooming problem even with unicast traffic has been shown to be NP-hard. In this paper, we introduce two novel approximation algorithms for the many-to-many traffic grooming problem. We also consider the routing and wavelength assignment problem with the objective of minimizing the number of wavelengths used. Through extensive experiments, we show that the proposed algorithms use a number of lightpaths that is very close to that of a derived lower bound. Also, we compare the two algorithms on other important objectives such as the number of logical hops traversed by a traffic stream, total amount of electronic switching at a node, and Min-Max objectives.

In this paper we present a new way to enhance the survivability of the information flow between two communicating nodes S and T without compromising the maximum achievable S-T information rate. To do this, bottleneck links should only forward useful information, and not redundant data units. We introduce the idea of extra source or destination connectivity with respect to a certain S-T max-flow, and then we introduce two problems: namely, pre-cut protection and post-cut protection. Because of space limitations we only focus on the pre cut protection problem. Specifically, we show that the pre-cut protection problem is NP-hard, we propose a heuristic approach to solve it, and we compare the performance of this heuristic to an ILP. Simulations show that the performance of the heuristic is acceptable even on relatively large networks.

In this paper, we introduce a low overhead scheme for the uplink channel allocation within a single cell of Cognitive Radio Wireless Mesh Network (CR-WMNs). The scheme does not rely on using a Common Control Channel (CCC). The mechanism is based on Physical layer Network Coding (PNC), in which two Secondary Users (SUs) are allowed to transmit synchronously over a randomly selected channel from a set of available channels, and without coordination for the purpose of requesting channels. The Mesh Router (MR) can detect up to 2 requests on the same channel due to the use of PNC, and replies back with a control packet which contains information about the assigned channel. We propose two PNC modulation schemes, PNC1and PNC2, where initially SUs choose one of them to employ through the network operation. Decoding the received signals in PNC1and PNC2depend on their received energy and phases shifts, respectively. Simulation results show that the proposed mechanism significantly outperforms traditional schemes that rely on using one CCC, or do not use PNC in terms of channel allocation time.

The use of wireless sensor networks is essential for implementation of information and control technologies in precision agriculture. We present our design of network stack for such an application where sensor nodes periodically collect data from fixed locations in a field. Our design of the physical (PHY) layer consists of multiple power modes in both the receive and transmit operations for the purpose of achieving energy savings. In addition, MAC layer is designed which uses these multiple power modes to save energy during the wake-up synchronization phase. We also present analytical models and simulation studies to compare the energy consumption of our MAC protocol with that of the popular S-MAC protocol and show that our protocol has better energy efficiency as well as latency in a periodic data collection application.

Two objectives of sensing in cognitive radio (CR) are to detect the primary user (PU) accurately and quickly, which are contradicting objectives. Therefore, many papers try to optimize this tradeoff and find the minimum sensing time which protects the PU. The trends are classified in enhancing false alarm probability (Pf) and detection probability (Pd), optimizing inter-sensing time, in-band sensing (monitoring) time optimization, and out-of-band sensing (search) time optimization. The PU model used in most of these work was a simple two states model (busy/idle renewal process). In this work, we developed a model for the PU in its idle state. The model enables the CR node to benefit from its previous measurements. It assumes that there are multi-idle states, each with specific length and known probability of staying in it. We used this model to find the best sensing time, energy detection threshold, and false alarm probability of the channel being sensed in monitoring. Also, we developed an out-of-band optimization formulation. The formulation finds the best number of channels to sense, the threshold of each channel, the sensing time of each channel, and Pfof each channel such that the PU is protected, the sensing time is minimized, and the CR will find an available channel with very high probability.

This paper introduces an efficient implementation of the network coding-based 1+N protection. The strategy provides proactive protection to N link-disjoint full-duplex connections against single link failures. The implementation is efficient and uses a tree shaped minimum cost protection circuit. The protection circuit carries linear combinations of data units originally transmitted on the working circuits, and these linear combinations can be used to recover data units lost due to failures. This recovery is carried out with the assistance of one node on the protection tree, which is chosen to reduce the recovery time. This protection technique requires the same amount of protection resources used by 1:N protection, where the protection circuit is link disjoint from the protected connections. The paper also makes other contributions. It introduces an Integer Linear Program (ILP) formulation to evaluate the cost of protection using this technique, and compares it to the cost of 1+1 protection. The comparison shows that a significant saving in cost can be achieved, while recovering from failures within a short time. The performance of this scheme is further evaluated using an OPNET-based simulation, where it was shown that the recovery time conforms to acceptable industry standards. Availability analysis is also conducted.

This paper introduces a network coding-based protection scheme against single- and multiple-link failures. The proposed strategy ensures that in a connection, each node receives two copies of the same data unit: one copy on the working circuit and a second copy that can be extracted from linear combinations of data units transmitted on a shared protection path. This guarantees instantaneous recovery of data units upon the failure of a working circuit. The strategy can be implemented at an overlay layer, which makes its deployment simple and scalable. While the proposed strategy is similar in spirit to the work of Kamal in 2007 2010, there are significant differences. In particular, it provides protection against multiple-link failures. The new scheme is simpler, less expensive, and does not require the synchronization required by the original scheme. The sharing of the protection circuit by a number of connections is the key to the reduction of the cost of protection. This paper also conducts a comparison of the cost of the proposed scheme to the 1+1 and shared backup path protection (SBPP) strategies and establishes the benefits of our strategy.

ABSTRACT The use of wireless sensor networks is essential for implementation of information and control technologies in precision agriculture. We present our design of network stack for such an application where sensor nodes periodically collect data from fixed locations in a field. Our design of the physical layer consists of multiple power modes in both the receive and transmit operations for the purpose of achieving energy savings. In addition, we design our MAC layer to use these multiple power modes to improve the energy efficiency of wake‐up synchronization phase. Our MAC protocol also organizes all the sender nodes to be synchronized with the receiver simultaneously and transmit their data in a time scheduled manner. Next, we design our energy aware routing strategy that balances the energy consumption over the nodes in the entire field and minimizes the number of wake‐up synchronization overheads by scheduling the nodes for transmission in accordance with the structure of the routing tree. We develop analytical models and simulation studies to compare the energy consumption of our MAC protocol with that of the popular S‐MAC protocol for a typical network topology used in our application under our routing strategy. Our MAC protocol is shown to have better energy efficiency as well as latency in a periodic data collection application. We also show the improvements resulting from the use of our routing strategy, in simulations, compared with the case when the next hop is chosen randomly from the set of neighbors that are closer to the sink node. Copyright © 2011 John Wiley & Sons, Ltd.

Cognitive radio networks (CRN) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. In this work, we consider the multicast routing and channel allocation problem in cognitive radio mesh networks. Due to the potential heterogeneity in channel availability among mesh routers (MRs) and the frequency switching latency, end-to-end delay and throughput degradation could be subject to a significant increase. We propose an on-demand multicast routing and channel allocation algorithm that takes channel heterogeneity and switching latency into consideration. The algorithm aims at reducing the end-to-end delay, and at the same time reducing the degradation of throughput using a dynamic programming approach.

The generalized 1+N protection, protects N link disjointly provisioned unicast connections by a single Steiner tree connecting all end points of the connections. By sending network coded packets on the protection Steiner tree in parallel with the working traffic, 1+N is able to recover from any single link failure without enduring the delay from switching to the backup path. This makes 1+N a better protection mechanism in terms of recovery time compared to traditional 1:N and in terms of protection capacity compared to 1+1 protection. Optimal cost provisioning and 1+N protection of a given set of connections is an NP-hard problem comprising of three NP-hard subproblems: partitioning of the connections, finding edge disjoint primary paths and Steiner tree protection circuit for the subset of connections in each partition. In this paper a polynomial time heuristic algorithm for 1+N protection is proposed which combines heuristic steps to address the three NP-hard components of the problem. Our simulations show that the heuristic algorithm provides average cost reduction of 29.2% and 18.5% compared to 1+1 protection in COST239 and NSFNET networks. An asymptotic bound is also derived for the case of complete graph networks which shows that 1+N can achieve maximum of 66.6% cost improvement compared to 1+1. When compared to the optimal 1+N solution from ILP formulation, the heuristic algorithm increases the cost no more than 13%.

In this work, we consider the multicast problem in a single cell in a cognitive mesh network. Due to the potential heterogeneity in channel availability among the members of a multicast group(s), the total multicast time could be longer due to transmitting the multicast data over multiple channels. We propose, in this work, an assisted multicast strategy with the objective of minimizing the total multicast time. This assistance is composed of two main activities, first, allowing the receivers in a multicast group to forward the data they have received to other members of the multicast group(s), and second, allowing the transmission of coded (bitwise XORed) packets so that receivers belonging to different multicast groups can decode and extract their data concurrently. We show, in this paper, that the proposed assistance paradigm achieves a considerable reduction in the total multicast time, which in turn increases the system throughput.

The p-cycle and its Failure Independent Path Protection (FIPP) extension are known to be efficient and agile protection strategies. The p-cycle is preconfigured such that if there is a failure, only the switches at two end nodes need to be reconfigured. In this paper, we extend the p-cycle by allowing cycles to have attached links, called Parasitic Protection Links (PPL), in order to protect paths whose source and destination nodes are not only located on the cycle but also connected by the PPL to the cycle. A p-cycle with PPL is named p2-cycle. We address the unicast service protection problems against single-link failures by using p2-cycle in mesh networks and the problem is formulated as an Integer Linear Program (ILP). The numerical results show that the p2-cycle scheme provides better capacity efficiency than the FIPP p-cycle scheme in all the traffic scenarios considered and consumes 2.7%-14.8% extra total cost over the optimum, provided by Shared Backup Path Protection (SBPP) approach. Moreover, we study the failure recovery performance by comparing it to FIPP and SBPP in terms of average number of reconfigurations (NOR). The results achieved by the p2-cycle is less than that of SBPP in every scenario considered and the number reduces from 2.48 to 2.31 comparing to 2, achieved by FIPP, as the traffic demands increases in NSFNET. In conclusion, the p2-cycle provides greater overall performance over existent p-cycle-based and path-based protection schemes, especially when the number of traffic demands is large.

A large number of network applications today allow several users to interact together using the many-to-many service mode. In many-to-many communication, a session consists of a group of users (we refer to them as members), where each member transmits its traffic to all other members in the same group. In this paper, we address the problem of dynamic provisioning of optical WDM networks to support many-to-many traffic grooming. The objective is to minimize the overall blocking probability of arriving many-to-many sessions. We address the problem in both non-splitting networks where the nodes do not have optical splitting capabilities and in splitting networks where the nodes have optical splitting capabilities. In each of the two networks, we propose a number of dynamic provisioning heuristics and we provide extensive experiments to evaluate and compare their performance.

A large number of network applications today allow several users to interact together using the many-to-many service mode. In many-to-many communication, also referred to as group communication, a session consists of a group of users (we refer to them as members), where each member transmits its traffic to all other members in the same group. In this paper, we address the problem of grooming sub-wavelength many-to-many traffic (e.g., OC-3) into high-bandwidth wavelength channels (e.g., OC-192) in WDM mesh networks. The cost of a WDM network is dominated by the cost of higher layer electronic ports (i.e., transceivers). A transceiver is needed for each initiation and termination of a lightpath. Therefore, our objective is to minimize the total number of lightpaths established. Unfortunately, the grooming problem even with unicast traffic has been shown to be NP-hard. For a number of special cases where the many-to-many traffic grooming problem is tractable, we efficiently derive the optimal solution, while in the general case, we introduce two novel approximation algorithms. We also consider the routing and wavelength assignment problem with the objective of minimizing the number of wavelengths used. Through extensive experiments, we show that the two algorithms use a number of lightpaths that is very close to that of a derived lower bound. Also, we compare the two algorithms on the several costs mentioned in the paper including the number of lightpaths and the number of wavelengths used.

Network fragmentation is a potential problem in wireless sensor networks (WSNs) due to many reasons like, node failures or environmental conditions (obstacles) that prevent connected deployments. One approach to cope with this problem is to have a mobile sink node (MS) patrol the network field and collect the data from all the fragments across the network. In this paper, we use a dynamic programming (DP) approach to determine the mobility trajectory of the MS within each fragment such that the energy consumption at the sensor nodes within the fragment is minimized. Moreover, we study the problem of finding the shortest route (cycle) that the MS should take in its journey between fragments in order to reduce a fragment's inter-visit time. For this purpose, we propose an Integer Linear Programming (ILP) formulation to find the optimal route. As finding the optimal route is NP-hard, we also propose a heuristic approach to find a near optimal solution.

Network fragmentation is a potential problem in wireless sensor networks (WSNs) due to many reasons like, node failures or environmental conditions (obstacles) that prevent connected deployments. One approach to cope with this problem is to have a mobile sink node (MS) patrol the network field and collect the data from all the fragments across the network. In this paper, we use a dynamic programming (DP) approach to determine the mobility trajectory of the MS within each fragment such that the energy consumption at the sensor nodes within the fragment is minimized. Moreover, we study the problem of finding the shortest route (cycle) that the MS should take in its journey between fragments in order to reduce a fragment's inter-visit time. For this purpose, we propose an Integer Linear Programming (ILP) formulation to find the optimal route. As finding the optimal route is NP-hard, we also propose a heuristic approach to find a near optimal solution.

The use of wireless sensor networks is essential to implementation of information and control technologies in application areas such as precision agriculture. We design MAC and Network layers for a wireless sensor network deployed for a precision agriculture application which requires periodic collection of sensor readings from fixed locations in a field. The Physical layer consists of a radio which operates in multiple power levels in the transmit mode and multiple sensitivity levels in the receive mode. The MAC layer is designed to save energy during the wake-up synchronization phase. The network layer is designed to custom fit the needs of the application, namely periodic data collection from fixed locations, and to minimize the energy consumption through balancing the communication load. The design of various protocol layers involves a cross-layer design strategy, taking into consideration the requirements and the characteristics of the application.

To support closed industrial control networks we consider constant end-to-end delay with minimal delay-jitter support in an asynchronous network. The Rate Constrained Static Priority rate-jitter (RCSP-RJ) minimizes jitter while not providing full restoration of traffic at each switch. RCSP-delay-jitter (-DJ) provides jitter free network communication but requires a means of time coordination between switches. We introduce FlexTDMA that allows nearly constant delay bounds with minimal delay-jitter in an asynchronous network. FlexTDMA uses a token bucket to restore the arrival envelope of each flow. This eliminates delay-jitter, simplifies analysis of the network and avoids cyclic dependent issues in network analysis. A network of FlexTDMA switches operate asynchronously and offer nearly constant delay and minimal delay-jitter. FlexTDMA adapts the functional characteristics of RCSP-RJ while providing the performance characteristics of RCSP-DJ, through the use of periodic baselining (transmission at maximal delay bound) of each flow at each switch. When a flow has been baselined the receiving switch establishes a reserved time slot in which the next frame arrival is expected (thus the term flexible TDMA). The value of a baseline event degrades relative to switch clock drift rates. Infrequent baseline collisions cause delay-jitter, and baseline transmission opportunities (TO) can be scheduled thereby minimizing the probability of alignment.

Broadband wireless access networks have received a tremendous amount of research and development in the recent years. There have also been pilot networks deployed in many cities around the globe. In the IEEE 802.16j standard, Relay Stations (RS) play a promising role of extending the range of a Base Station (BS). This architecture is suitable to areas with limited infrastructure, such as rural areas, since it is difficult to install many BSs, with each having a wired connection. In this paper, we present an optimization model that finds the number of RSs and their locations to serve a customer base. We also show how our model can be adapted to make the planning in real-life scenarios where there are obstacles, such as mountains and lakes, in the planning area.

A large number of network applications today allow several users to interact together using the many-to-many service mode. In many-to-many communication, also referred to as group communication, a session consists of a group of users (we refer to them as members), where each member transmits its traffic to all other members in the same group. In this paper, we address the problem of designing and provisioning of WDM networks to support many-to-many traffic grooming. Our objective is to minimize the overall network cost, which is dominated by the cost of higher layer electronic ports (i.e., transceivers) and the number of wavelengths used. Based on different WDM node architectures, we propose four different WDM networks for many-to-many traffic grooming. For each network, we analyze the many-to-many traffic grooming problem and provide an optimal as well as a heuristic solution. A comprehensive comparison between the four networks reveals that each of the networks is the most cost-effective choice for a certain range of traffic granularities.

A large number of network applications today allow several users to interact together using the many-to-many service mode. A many-to-many session consists of group of users (we refer to them as members), where each member transmits its traffic to all other members in the same group. We address the problem of designing and provisioning of WDM networks to support many-to-many traffic grooming. Our objective is to minimize the overall network cost which is dominated by the cost of transceivers and the number of wavelengths used. We consider three different WDM networks for this problem. One is the non-splitting network, where the nodes do not support optical splitting. The other two networks are the hubbed and the all-optical networks, where the nodes support optical splitting. In the hubbed network, all members in a session transmit their traffic to a designated hub node. Using the new technique of network coding, the hub then linearly combines the traffic units received and sends back to the members a set of linear combinations using light-tree(s). In the all-optical network, each member in a session transmits its traffic directly to all other members in the same session using a light-tree. A comprehensive comparison between the three networks reveals that each of the networks is a cost-effective choice for a certain range of traffic granularities.

We revisit the problem of scheduling the sources transmissions in a many-to-one flow to provide reliable communication between n sources and a single destination. The performance of coding-aware scheduling is studied based on both digital network coding (DNC) and analog network coding (ANC). We discuss some special cases in which an optimal ANC-based schedule can be constructed efficiently. Finally, we show that the maximum gain from using ANC is theoretically bounded by n, where n is the number of sources.

Repairing connectivity and achieving a certain level of fault tolerance are two important research challenges in wireless sensor networks that have, in many papers in the literature, been jointly studied. Most of the proposals that aim at restoring network connectivity deal with the network as a general graph of N nodes with the edge cost being the number of nodes needed to establish connectivity between the two ends of the edge. This assumption ignores the topological properties of the network, especially the overlap between sensors' communication ranges, and the node-failure pattern that caused the disconnection. In this paper, we try to exploit these properties to minimize the number of additional nodes needed to repair the connectivity.

With the enormous increase in the usage of communication networks, protection against node and link failures became essential in the deployment of reliable networks. To manage loss of data due to node failures, we design a network protection strategy that aims to withstand such failures. In this paper we propose a protection strategy against any single node failure in the network. We deploy network coding and reduced capacity in this strategy without adding extra working paths to the readily available connection paths. The strategy is based on representing node failures by the failure of multiple links, and hence is transformed into a protection against multiple link failures. Consequently, the protection strategies proposed in, can also be applied for protection against node failures. In addition, we demonstrate the implementation aspects of the premeditated protection strategies.

This paper addresses the problem of survivability of many-to-one flows in wireless networks, such as wireless mesh networks (WMNs) and wireless sensor networks (WSNs). Traditional protection schemes are either resource-hungry like the (1+1) protection scheme, or introduce a delay and interrupt the network operation like the (1 : N) protection scheme. In this paper, we present a network coding-based protection technique that overcomes the deficiencies of the traditional schemes. We derive and prove the necessary and sufficient conditions for our solution on a restricted network topology. Then we relax these connectivity requirements and show how to generalize the sufficient and necessary conditions to work with any other topology. We also show how to perform deterministic coding with {0,1} coefficients to achieve linear independence. Moreover, we discuss some of the practical considerations related to our approach. Specifically, we show how to adapt our solution when the network has a limited min-cut; we therefore define a more general problem that takes this constraint into account, which prove to be NP-complete. Furthermore, we discuss the decoding process at the sink, and show how to make use of our solution in the upstream communication (from sink to sources). We also study the effect of the proposed scheme on network performance. Finally, we consider the implementation of our approach when all network nodes have single transceivers, and we solve the problem through a greedy algorithm that constructs a feasible schedule for the transmissions from the sources.

In this paper, we present a new technique that uses deterministic binary network coding in a distributed manner to enhance the resiliency of sensor-to-base information flow against packet loss. First, we show how to use network coding to tolerate a single packet loss, and then we extend the solution to tolerate multiple losses. Moreover, we study the coding efficiency issue and introduce the idea of relative indexing to reduce the coding coefficients overhead. Finally, we show through a simulation study that our approach is highly scalable and performs better as the network size and/or number of sources increases.

Exploiting mobility to enhance the performance of wireless sensor networks (WSNs), in terms of connectivity, coverage, and lifetime elongation, has recently been the focus of several research studies. Mobility was adopted in two different ways; either using a network of mobile sensor nodes or deploying a few supplementary special mobile elements, usually referred to as mobile agents to help enhance connectivity and coverage. Different modes of operation (roles) were assigned to mobile agents including being a data relay, data collector, and data sink. In this paper we use a closed queueing network to model mobility and then evaluate data latency under all those roles. The proposed model provides powerful means to understand the effect of different parameters, like velocity and number of mobile agents as well as their movement strategy, on data latency.

Protecting against link failures in communication networks is essential to increase robustness, accessibility, and reliability of data transmission. Recently, network coding has been proposed as a solution to provide agile and cost efficient network protection against link failures, which does not require data rerouting, or packet retransmission. To achieve this, separate paths have to be provisioned to carry encoded packets, hence requiring either the addition of extra links, or reserving some of the resources for this purpose. In this paper, we propose network protection codes against a single link failure using network coding, where a separate path using reserved links is not needed. In this case portions of the link capacities are used to carry the encoded packets. The scheme is extended to protect against multiple link failures and can be implemented at an overlay layer. Although this leads to reducing the network capacity, the network capacity reduction is asymptotically small in most cases of practical interest. We demonstrate that such network protection codes are equivalent to error correcting codes for erasure channels. Finally, we study the encoding and decoding operations of such codes over the binary field.

Optimal network provisioning is the process of equipping the network with the devices and resources needed to support all traffic demands while minimizing the network cost. Optical amplifiers (OAs) are one of those devices that contribute significantly to the network cost, especially in wide area networks. We propose a heuristic approach for placing the minimum number of OAs in the network. The solution is designed for all-optical multicasting and is based on dividing the problem into smaller subproblems. While solving them separately, the heuristic takes the interdependency between these subproblems into consideration. Therefore, efficient and accurate solutions can be obtained which is shown by the numerical results.

In many-to-many communication, a session consists of group of users (we call them members) where each one of the members transmits its traffic to all other members in the group. This paper studies the problem of provisioning many- to-many sessions with sub-wavelength granularities in WDM mesh networks. Our objective is to minimize the number of transceivers required. We study the problem in networks with and without optical splitting capabilities. For networks without optical splitting capabilities, we use an optimal approach which is an extension of that introduced in [8] for the many-to-one traffic grooming problem. For networks with optical splitting capabilities, we introduce a novel hub-based approach where each session is routed through a many-to-one tree from the members to a central hub node, and then through a multicast tree from the hub node back to the members. At the hub node, network coding is performed by linearly combining the traffic units received from the members. These combinations are then groomed and delivered back to the members using light-tree(s). Numerical results from both approaches are presented and compared.

1+N protection was proposed by the author in [1], where a single circuit is used to protect a number of connections. For this purpose, network coding was used to carry a linear combination of the signals, and p-Cycles were used to provide this combination to the destination nodes in order to allow them to extract a second copy of the transmitted signal. In this paper, we introduce a generalized form of 1+N protection. Network coding is used to combine a number of signals on one protection circuit. However, p-Cycles and not used, and the signals are combined on a protection circuit, which is optimally selected to minimize the amount of protection resources. Moreover, and unlike the strategy in [1], the generalized strategy does not require symmetry in resource reservation in the two directions of communication, but the protection resources are provisioned based on need. The strategy introduced in this paper provides 100% protection against single link failure on any of the working paths. A performance comparison between the cost of implementing the proposed scheme and 1+1 protection is provided, and is based on integer linear programming formulations.

In this paper we address the problem of traffic grooming in WDM rings with all-to-all and its generalization to many-to-many service by using network coding. We consider minimizing the number of Line Terminating Equipment (LTE) on two types of unidirectional rings, namely, single-hub and un-hubbed rings, as our objective. In single-hub rings, we investigate the minimum cost provisioning of uniform all-to-all traffic in two cases: where network coding is used to linearly combine data, and where it is not used and data is transmitted without coding. We generalize the service mode to many-to-many and evaluate the cost of provisioning. In un-hubbed ring, we propose a multi-hub approach to obtain the minimum cost provisioning in the case of all-to-all and many-to-many traffic. From our numerical results, we find that under many to many traffic, network coding can reduce the network cost by 10-20% in single-hub rings and 1-5% in un-hubbed rings.

In [1], the author introduced a strategy to use network coding on p-Cycles in order to provide 1+N protection for straddling connections and links against single link failures in mesh optical networks. In this paper we extend the work in [1] and introduce a GMPLS-based implementation of this strategy for link protection, that is also used to protect on-cycle links. We refer to this scheme as hybrid 1+N protection, since some data units are transmitted without being combined with other data units. The strategy uses a combination of GMPLS standard label switched paths (LSP) for protecting on-cycle links, and modified LSPs, which we refer to as label switched cycles (LSC), for protecting straddling links. The strategy does not have to explicitly detect failures, but rather detects the absence of data units. Destinations receive backup copies of packets within no more than the p-Cycle propagation delay for on-cycle link failures, and no more than the sum of the propagation delays of the p-Cycle and the longest straddling link for straddling link failures. The implementation details of this strategy are presented, and the cost of implementation, in terms of link usage is evaluated and is shown to be modest. This scheme can therefore provide protection at higher layers, at a speed that is comparable to that achieved at the physical layer, but at a much reduced cost, while being flexible, and requiring less involvement from the management and control planes.

In [1], the author presented a 1+N protection strategy against single link failures using a network coding approach on p-Cycles. In this paper, we extend this approach to protect against multiple link failures. For the network to be protected againstMlink failures,Mp-Cycles are used. The connections sharing a certain cycle must have link disjoint paths, and they encode their transmitted and received data units on two counter rotating half cycles on each of the two cycles. To recover from m link failures usingMcycles, where 1lesmlesM, the data units are encoded on the cycles in such a way that each node affected by failures should recover m linearly independent combinations of the m units affected by the failures. To illustrate the concept, we show how to protect against two link failures, and describe in detail the encoding and decoding processes.

Accurately simulating user movements in mobile ad hoc networks (MANETs) is very important to the prediction of actual network and user performance. Therefore, using a realistic mobility model is a crucial factor in enhancing the confidence in the simulation results of these networks. In this paper, we present a new mobility model for MANETs, called realistic mobility model (RMM). In RMM, node velocities and directions of movement are taken from probability distributions that mimic real user mobility behavior. The model is shown to be stationary since it satisfies the conditions of (J.Y. Lee et al., 2005). Simulation results show that RMM produces mobility traces that closely resemble real mobility traces.

In this paper we consider traffic routing in 802.11- based multi-hop wireless mesh networks (WMNs). Interest in such networks arises since they offer flexible, and cost effective means of providing Internet connectivity to communities of subscribers. Successful deployment of such networks, however, hinges on the ability of the network to serve subscribers at the data rates specified by service agreements, as well as providing quality of service to certain key traffic types, such as TCP traffic, delay-jitter sensitive traffic, and traffic that requires synchronized delivery to end users. Since delays on different routes in such networks may vary widely, routing of the above traffic types can potentially benefit from non-bifurcated routing schemes that do not split flows among multiple paths. In this paper, we formalize the problem of non-bifurcated routing, while meeting subscriber demands, as an optimization problem. We present a heuristic algorithm that utilizes results from the theory of maximum flows, and insights into the routing problem to obtain efficient solutions. Simulation experiments indicate improved achieved throughput, and delay-jitter results over the use of the standard Dynamic Source Routing (DSR) algorithm.

Survivability of many-to-one flow networks such as wireless mesh networks (WMNs) and wireless sensor networks (WSNs) is an important issue that has not received enough attention in the literature. Traditional proactive and reactive protection schemes are either resource-hungry like the (1 + 1) protection scheme, or introduce a delay and interrupt the network operation like the (1 : N) protection scheme. In this paper, we present a novel approach that relies on network coding to provide protection to many-to-one flows as in WMNs or WSNs at the speed of proactive protection, but at the cost of reactive protection. We derive and prove the necessary and sufficient conditions for our solution on a restricted network topology. Then we introduce three generalizations of our problem, and provide a heuristic and a mixed integer linear program (MILP) to solve one of them. We also show how to perform deterministic coding with {0,1} coefficients to achieve linear independence. Finally, we discuss some practical considerations related to our approach, and define a more general problem that takes these considerations into account. We also formulate this general problem as an MILP.

Due to the wide range of applications in sensors and Wireless Sensor Networks (WSN), research in this area has recently received increasing attention. WSNs rely on network connectivity to deliver data to a base station through multihop communication. However, connectivity may not be always achievable for a number of reasons. In this paper, we study the problem of data delivery in disconnected WSNs. A special class of disconnected sensor networks called "Fragmented wireless sensor networks (FWSN)" is considered. A FWSN consists of several groups of connected sensors that we call "fragments". To achieve connectivity between these fragments, mobile agents move in the network and act as data relays between fragments, in order to eventually deliver data to the base station. The main contribution of this paper is the modeling of the movement of these mobile relay nodes as a closed queueing network to obtain steady state results of the distribution of the mobile relays in the network. Building on these results, we derive the distributions of the fragment-to-fragment, and fragment-to-sink delays. Comparing these analytical results to results from the TOSSIM simulator, it is shown that this model accurately captures the system behavior, and can be used to predict data delivery delays.

p-Cycles have been proposed for pre-provisioned 1:N protection in optical mesh networks. Although the protection circuits are preconfigured, the detection of failures and the rerouting of traffic can be a time consuming operation. Another survivable mode of operation is the 1+1 protection mode, in which a signal is transmitted on two link disjoint circuits, and the destination chooses the strongest of the two signals, hence recovery from failures is instantaneous. The disadvantage is the large number of protection circuits. In this paper we introduce a new concept in protection: 1+N protection, in which a p-Cycle can be used to protect a number of bidirectional connections. Data from different circuits are combined using network coding, which can be implemented in a number of technologies. The maximum outage time under this protection scheme is limited to no more than twice the p-Cycle propagation delay.

Passive optical networks (PONs) is the answer to increasing demand on bandwidth and an enabler for Fiber To The x (FTTx) implementation, where x can be Home (FTTH), Curb (FTTC), or Building (FTTB). However, PONs use shared fiber link with broadcast and select type of downstream traffic on single wavelength, which makes it vulnerable to eavesdropping. Security at network level in addition to cryptography is vital to success of FTTx in order to make collection of sample traffic difficult. In this paper, a novel technique that consists of slow wavelength hopping and wavelength sequences cycling techniques are suggested where each Ethernet frame/packet is encoded by single wavelength. Each optical network unit (ONU) will have its own wavelength sequence generated by mapping code matrices to a single network wavelength grid matrix. Multiple wavelengths sequences assignment to a single ONU are cycled in different orders so as to enhance security in order to reduce probability of tracking hopping pattern. This technique can also be combined with a block cipher encryption technique, such as AES, in order to increase the security of the encryption algorithm

In this paper we consider the optimal design and provisioning of WDM networks for the grooming of multicast subwavelength traffic. We develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming. We also introduce heuristic solutions. Optimal solutions are designed by exploiting the specifies of the problems to formulate Mixed Integer Linear Programs (MILPs). Specifically, we solve the generic multicast problem in which, given a set of multicast sessions and all destination nodes of a multicast session requiring the same amount of traffic, all demands need to be accommodated. The objective is to minimize the network cost by minimizing the number of higher layer electronic equipment and, simultaneously, minimizing the total number of wavelengths used. We also solve two interesting and practical variants of the traditional multicast problem, namely, multicasting with partial destination set reachability and multicasting with traffic thinning. For both variants, we also provide optimal as well as heuristic solutions. Also, the paper presents a number of examples based on the exact and heuristic approaches

Most of the network applications bandwidth requirements are far less than the bandwidth offered by a full wavelength in WDM networks. Hence, traffic grooming is needed to make efficient use of the available resources. In this paper we address the grooming of many-to-one traffic demands in WDM networks on arbitrary topologies. Traffic streams from different sources, but part of the same session and thus terminating at the same destination, can be aggregated using arbitrary, but application dependent, aggregation ratios. We provide optimal as well as heuristic solutions to the problem. The objective is to minimize the cost of the network, by minimizing the total number of the higher layer components and the total number of the wavelengths used in the network. One of the main contributions of this work is to provide a mixed integer linear solution, to an otherwise non-linear problem, by exploiting the specifics of routing and aggregation sub-problems, while still maintaining the optimality of the solution. The formulation is generic and can handle varying amounts of traffic from each source to a common destination, as well as arbitrary aggregation fractions of the data coming from the different sources. This fraction is made to be a function of the number of the streams participating in the aggregation. For the heuristic solution we developed a Dynamic Programming style approach that builds the solution progressively, going through a number of stages, while choosing the best partial solutions among a number of possible partial solutions at each stage

We explore network design principles for next-generation all-optical wide-area networks, employing light-trail technology. Light-trail is a light-wave circuit that allows multiple nodes to share the optical bandwidth through the inclusion of simple but flexible hardware overlaid with a lightweight control protocol. We develop light-trails as a novel and amenable control and management solution to address IP-centric communication problems at the optical layer. We propose optical switch architectures that allow seamless integration of lightpath and light-trail networks, and assess their costs and capabilities. We formulate the static light-trail RWA problem as an integer linear program. Since this programming problem is computationally intractable, we split it into two subproblems: (a) trail routing, for which we provide three heuristics, (b) wavelength assignment, for which we use the largest first heuristic available in literature. The objective of our design is to minimize the optical layer and electronic layer costs in terms of the number of wavelengths and communication equipment required. We illustrate our approach by comparing the performance of our trail design heuristics on some test networks.

In this paper, we study the routing and wavelength assignment (RWA) problem of a set of multicast sessions on already dimensioned wavelength routed network, while taking optical power impairments into consideration. Our main objective is to minimize the session blocking probability. We formulate the problem as a mixed-integer linear program (MILP). A greedy algorithm is also introduced, which provides fast, yet efficient, solutions for large problem sizes. The CPLEX linear optimization package is used to solve the MILP formulation, while simulation was used to experiment with the greedy algorithm.

In first mile networks, traffic may belong to different priority classes, which have different levels of delivery urgency. In this paper we introduce a mechanism for network wide prioritized access in IEEE 802.3ah EPONs which use the MPCP protocol. The mechanism is simple, and is compatible with the standard MPCP protocol. The basic ONU protocol does not have to be changed, while the protocol used at the OLT side requires little, and simple changes. Several objectives of priority mechanisms are stated, and the developed mechanism is shown to satisfy the stated objectives through a simulation study. The proposed protocol can be used with any dynamic bandwidth algorithm for EPONs.

Recently, a new architecture called light-trails has been proposed that provides a novel control and management solution to address IP-centric issues at the optical layer. By inclusion of simple hardware that performs drop and continue functionality, overlaid with a light-weight control protocol, light-trails enable efficient sharing of network resources, improve bandwidth utilization and minimize network costs. Due to power budget constraints in such networks, it may not always be possible to have end to end communication in pure optical domain and requests may be required to traverse multiple intermediate transit points called hub nodes before reaching the final destination. The hub nodes need to be equipped with special hardware for switching and grooming connections. We investigate the problem of designing networks where such hubs are sparsely located. We show through our simulation results that by carefully designing heuristics for hub node placement and trail routing, it is possible to achieve high throughput with minimal number of hub nodes.

A mobile ad hoc network (MANET) can be represented by a set of logical clusters with clusterheads (CHs) acting like virtual base-stations, hence forming a wireless virtual backbone. The role of clusterhead is a temporary one, which changes dynamically as the topology or other factors affecting it change. Finding the minimal set of CHs is an NP-complete problem. We study the performance tradeoffs between two clustering approaches. The first one is a simple clustering strategy, called virtual grid architecture (VGA), which is based on a fixed rectilinear virtual topology, while the second one is an optimal clustering strategy. We consider homogeneous as well as heterogeneous networks. First, for homogeneous MANETs with a large number of users and under the VGA clustering approach, we derive expressions for the number of CHs, worst case path length, and average case path length. We also derive expressions for the communication overhead. Second, we develop an integer linear program (ILP) that finds the optimal number of connected CHs in small to medium sized heterogeneous MANETs. Analytical and simulation results show that our proposed clustering algorithm (VGA), although being simple, is close to optimal.

Power loss encountered by the optical signal in the light-trees can be due to attenuation, signal splitting, or both. Optical amplifiers may have to be used in order to recover from the power loss so that the optical signal can still be detected at the receivers. Due to the high cost of optical amplifiers, we introduce an optimal power-aware design of wavelength-routed optical networks supporting multicast traffic. The network design problem is formulated as a mixed-integer linear programming, and the main objective is to minimize the total required gain in the network while allowing unequally powered signals. It also determines the optimal number and placement of the splitters, and the routing and wavelength assignment for the multicast sessions. We used the CPLEX linear optimization package in order to solve the MILP formulation. Then, the number and location of optical amplifiers per link is computed off-line.

The ability of mobile ad hoc networks (MANETs) to support quality of service (QoS) guarantees is constrained by the scarce bandwidth of the wireless medium and the free node mobility. One consequence of free node mobility is that network topology may change frequently over time. QoS routing is the first step toward achieving end-to-end QoS guarantees in MANETs. However, topological changes cause frequent route breakage and therefore topology stability becomes a very important requirement for QoS routing. To minimize route breakage, it is important to find stable routes that exhibit long lifetimes. We propose an approach to support QoS routing in MANETs. Our approach creates a fixed rectilinear wireless virtual backbone that is robust and stable against topological changes. We examine the stability feature of the proposed virtual topology and study its effect on the ability of the network to support QoS guarantees. We also analyze the connectivity of the proposed virtual architecture, which is a basic requirement for network reliability. Our results show that the proposed model is able to provide more stable QoS routes with delay guarantees. Moreover, packet delivery and call acceptance ratios increase under the proposed scheme.

A fundamental challenge in the design of wireless sensor networks (WSNs) is to maximize their lifetimes. Since many sensors have correlated readings, data aggregation has emerged as an efficient approach to reduce the number of transmissions, and hence minimize overall power consumption in the network. An important aspect of data aggregation is the placement of aggregation points and how aggregated data is routed to the gathering points. In this paper, we consider the problem of correlated data gathering in WSNs with the objective of minimizing the total transmission cost in terms of power consumption. We particularly focus on the problem of finding the set of aggregation points that satisfy our objective. Two solution strategies are presented: an exact solution using an integer linear program (ILP) formulation and a near optimal, but simple and efficient heuristic, called load balancing with aggregation (LISA). Our solutions use a fixed virtual wireless backbone that is built on top of the physical topology. Numerical results show that our proposed scheme provides substantial energy savings when compared to other schemes.

In This work we consider the optimal dimensioning of optical networks for multicast traffic grooming problems on WDM networks under two practical scenarios. In both cases, for each multicast session the destination set consists of two disjoint subsets. In the first scenario only one subset of each multicast session must be accommodated while the other subset can only be accommodated if this results in no additional cost. In the second case, both subsets of each multicast session must be accommodated. However, each subset has different bandwidth requirements. We develop optimal and heuristic solutions for both the cases.

The proportional differentiation model is a newly introduced approach for differentiated services networks. This paper proposes and evaluates a scheduling mechanism for the combined control of delay and throughput metrics, according to the proportional differentiation model. The scheme is based on the well known Little's law. A moving window averaging mechanism and an active queue management scheme are simultaneously, and respectively used to achieve control over the relative throughputs as well as the relative delays between classes. The scheme does way with measurement of the actual packet delays, and state information is minimized. Some feasibility bounds are presented, and a simulation study shows the effectiveness of this scheme.

Congestion handling in the TCP protocol is through implicit feedback about the network conditions to the traffic source. If the feedback can be obtained sooner at the source, the congestion handling can be more effective. The goal of this work is to expedite the feedback process and enhance the performance of the Internet under congestion. We motivate the need for the proposed schemes with the help of a simplified analytical model which evaluates the time to detect a packet loss through duplicate acknowledgements. We show that this time can be significant when the number of flows going through a router is large. We then present two schemes, Triple Packet Buffering on Congestion, and Triple Truncated Packets, that enables congestion information to be fed back to the source faster. This results in a faster response to congestion conditions, and a faster retransmission of discarded packets. With the help of simulation results, we show that the proposed schemes perform better than the standard implementation in a variety of network scenarios and achieve higher throughput and lower mean delay. This scheme also does not require any changes in the standard TCP protocols, but a simple (optional) modification at the routers.

We present the design and modeling of an intelligent modular switching architecture, designated MSA, for packet transport over next-generation ultra-dense wavelength division multiplexed (WDM) networks. The proposed architecture reduces the electronic processing overhead in sending the packets from source to destination. This is achieved by sending routing/forwarding information on a dedicated control channel. The control packets are processed electronically by a control processor at each intermediate node. The processing of control packets ensures that the data packets propagate from source to destination in photonic form (that is, without any processing by intermediate nodes). Due to the flexible nature of the proposed architecture it can be applied to support opaque optical networks to all-optical networks and can be used to transport IP/MPLS and ATM traffic. The performance evaluation of the proposed switching architecture shows that it drastically reduces the electronic processing overhead. Saving in the processing overhead can leverage bandwidth utilization without increasing the hardware gear cost. Furthermore, a large saving in terms of electrical-to-optical (E/O) and optical-to-electrical (O/E) converters cost per node can also be achieved.

In a mobile environment, each mobile host should have a home agent on its home network that maintains a registry of the current location of the mobile host. This registry is normally updated when a mobile host moves from one subnet to another. We study the tradeoff between the cost of updating the registry and the cost of searching for a mobile host. Using a set of special agents, called proxy agents, which implement a two-tier update process, the cost of updates could be reduced; however, the search cost might increase. We introduce a mathematical programming approach to identify a set of proxy agents that minimizes the cost of search. We formulate the minimization of the cost as Min-Sum, Min-Max, and Min-P problems. For situations when fast solutions that are not necessarily optimal are more desirable, we propose using clustering heuristics. Results of many experiments are presented.

This paper investigates the possibility of using wavelength division multiplexing to enhance the system capacity of local area networks with the bus topology. We first consider the choice between wavelength fixed and wavelength tunable transceivers based on practical considerations, and we conclude that the use of fixed wavelength transceivers allows one to employ WDM while still using standard interface cards. This requires that wavelength translation be implemented in the network. The paper then considers the problem of wavelength translation and introduces three approaches: the min-max approach that minimizes the maximum load on channels; the Translation at Destination approach in which wavelength translation is done only at the ingress point to the receiving channel, and the Translation at Source approach in which the wavelength translation is done at the egress point from the source. Practical algorithms for implementing these optimal allocations are considered. We then present throughput analysis of dual-bus networks employing the proposed translation approaches.

This paper introduces a method for obtaining the steady state probabilities in G/sup [X]//D/C/K type queues. The solution method is more efficient than other known techniques in terms of both time and space requirements. The method is an extension of the MBH technique used to solve single server queues, It is shown that this method allows one to perform a parametric study quickly and efficiently by building on already existing results. The solution technique is used to evaluate the performance of concentrators used in ATM networks where low speed lines are connected to higher speed ones, e.g., 150 Mb/s lines to a 620 Mb/s or 1.2 Gb/s line. The concentrator is equipped with a finite capacity buffer, and is modeled as a synchronous multiple server queue with finite buffer. The concentrator is fed by a discrete batch Markov arrival process (D-BMAP) which is capable of capturing the traffic characteristics of a broad range of applications. The paper derives the probability of cell loss introduced by the concentrator buffering, and considers the effect of the buffer size as well as the line speeds on this measure.

The ATM adaptation layer type 5 (AAL5) is an adaptation layer protocol for the ATM layer of broadband integrated services digital networks (BISDNs), which targets connection-oriented services. Among several features, it is unique in the sense that it includes an end-of-packet (EOP) indicator in the header of the ATM cell (the ATM-layer-user-to-ATM-layer-user (AUU) parameter in the payload type (PT) field). It was suggested that the use of this indicator may be used to provide a means by which buffer occupancy can be reduced by selectively dropping cells from already incomplete packets [Armitage and Adams, 1993]. The objective of this paper is to study the performance of this layer and the effectiveness of the EOP indicator. The performance measures of interest are the probability of packet loss and the mean packet delay. An approximate analytical model is constructed in which the blocking of a tagged source is kept track of in an exact manner. The rest of the sources are modeled approximately. The accuracy of the model is enhanced through an iterative approach. A simulation model is also constructed to assess the accuracy of the approximate model.

This paper presents a scheme that manages bandwidth allocation and deallocation in ring local and metropolitan area networks. The scheme is based on dividing the total ring bandwidth into a number of 64 kbits/sec channels. Isochronous channels are allocated out of this total bandwidth in a dynamic manner. The maximum isochronous bandwidth that can be allocated is theoretically limited by the ring bandwidth. Release of isochronous channels is also implemented dynamically and in a distributed manner, with minimal intervention from the monitor station. In addition, the remaining bandwidth can be used for asynchronous communication. The scheme also allows a minimum amount of bandwidth to be reserved for asynchronous communication. Another feature of this scheme is that bandwidth portions used by isochronous and asynchronous channels do not interleave, and that the isochronous channels allocated to a certain call are contiguous. The paper also shows how this scheme can be adapted to the FDDI-II network.>

The authors respond to section III of the above-titled work (ibid., vol.42, no.6, pp.735-737, June 1993), in which it is shown that their medium access control (MAC) protocol for rings (bid., vol.39, no.3, pp.289-299, Mar. 1990) cannot be implemented with minimum delay. They point out some differences in terminology and provide further clarification of their work.>

Consider an arbitrary number of stations equally spaced on a channel configured as a bus topology. The stations access the channel according to the nonpersistent CSMA protocol, which may be with or without collision detection. Users are synchronized to the propagation delay between two adjacent users, and are thus semisynchronized. Such a system with single-buffered users and an exponential backoff strategy, is modeled. The system throughput and the mean delay are evaluated, and it is shown how to obtain the distribution of the packet interdeparture time as well as the packet delay. It is shown that the rescheduling strategy has a profound effect on the station performance, and that it can be used to eliminate the difference between the performance of different stations at any given offered load. The model is also used to accommodate finer synchronization steps, as well as arbitrary topologies.>

A strategy for ring networks using multiple tokens is defined. The tokens are released either at their sources or at their destination nodes, depending on bandwidth availability and frame lengths. This results in greater bandwidth availability and reduced access delays, as confirmed by simulation results. The protocol is shown to outperform several other popular protocols, like the token ring with early token release, e.g. FDDI, the slotted ring, and DQDB. This performance superiority is shown to hold over almost the entire range of traffic loads, with different network spans and different values of average packet lengths.>

The slotted ring local area network is very efficient under light load, but suffers from severe performance degradations as the load increases, especially if message sizes are highly variable. An algorithm is presented, in the form of a medium access protocol, to allow for the efficient utilization of the bandwidth of the slotted ring. The protocol is based on the release of slots by destination stations of every slot, except for the first and last slots of a message transmission. This means that station latencies do not have to be increased. As a direct consequence of this property, the source and station identification do not have to be included in every slot, therefore reducing the message overhead. The algorithm results in an improved performance over the entire workload range, and under most workload conditions, therefore illustrating how far the performance of the slotted ring can go.>

An analysis of the throughput achieved by broadband nonpersistent carrier sense multiple access (CSMA) on the bus topology is discussed. The transmission is unidirectional, with the head-end being either active or passive. A homogeneous infinite number of users is assumed to be uniformly distributed on the bus. It is shown that although unidirectional broadband transmission allows for higher transmission rates, and longer distances, the normalized throughput achieved is less than that achieved by bidirectional broadband or baseband transmission.>

This work carries out an exact analysis of carrier sense multiaccess (CSMA) on the bus topology using fine-grain synchronization; that is, synchronization on the order of the distance between adjacent stations. The author models a system with a finite number of users transmitting on a bus using CSMA without collision detection. The model can accommodate any arbitrary station distribution. He evaluates the throughput of the system and shows how the station location on the bus affects the throughput achieved by the different stations. The model is also used for a slightly modified version of the CSMA protocol with collision detection.>

A modification is presented of the distributed-queue dual-bus (DQDB) access protocol that allows: (1) messages to be transmitted with a minimal amount of overhead, and (2) slots to be released by the destination stations, without increasing the stations latencies, and without the use of erasure nodes. The allowance for a minimal overhead to be included in multi-segment message transmissions is provided through the use of the continuation-bit approach, while the slot release by destination stations, without increasing the stations latencies, is made possible by having stations keep track of the active message transmissions. This protocol requires few, but not major, changes to the slot format of the DQDB. The performance of the new protocol compares favourably to that of the original DQDB protocol and the DQDB protocol with erasure nodes.>

A strategy, called the continuation-bit approach, for scheduling the transmission of variable-length (multi-packet) messages on slotted high-speed LAN/MAN (local/metropolitan area networks) is studied. Its overhead is analyzed and compared with that of a conventional slotted system. Then, the authors apply this approach to the p/sub i/-persistent protocol, which is an efficient unity-capacity protocol proposed for high-speed LAN/MAN. Specifically, approximate analytical models for light and for heavy traffic loads are formulated, and from these models the proper network operating parameters p/sub i/ are determined. The authors consider several examples to study the characteristics of the continuation-bit approach, and verify the accuracy of the approximations via simulation.>

A simulation model is developed from which the effect of the retry time on the performance of Hubnet is studied. Results show that increasing the retry time does not always mean higher delay. It is shown that, for retry time values that are less than and are factors of the packet lengths, the mean delay is better than some lower retry time values subject to some range. Also, it is shown that, contrary to what was believed earlier, a packet in a Hubnet with a retry time which is a factor of the packet length is guaranteed transmission rights. These results are used to study a Hubnet with two priority classes, where the retry times are used for priority assignment. It is shown that a lower retry time does not always mean higher priority. It is shown that a class of users with a retry time value that is a factor of the packet length could have higher priority than a class of users with a lower nonfactor retry time value.>

A solution to the token ring problem of lowered efficiency during light and moderate loads is proposed. It is shown how the medium access can be managed in a distributed way, using two tokens. The overhead is kept to a minimum. It is shown that the performance of this protocol is superior to that of the single-token ring protocol under light to medium loads, while it is slightly worse at higher loads. The protocol is then extended to a greater number of tokens. It is shown that increasing the number of tokens improves the performance at light load, but degrades the performance at high load.>

A slotted-ring protocol that performs well across the full range of message length distributions is presented. The relative performance of the protocol is best at low to medium ring utilization, which is the most usual operating condition for local area computer networks. The protocol is not subject to the normal requirement for repeating source and destination addresses in each slot of a multiple-slot message. This reduced overhead feature is a main reason for the performance gains that are achieved. The protocol does not depend on any central control station for assigning slot usage to individual stations. However, it does require each ring station to keep track of the current status and source station usage of each slot on the ring. Implementation of the protocol would require significantly more complex logic circuits than are normally needed in either token rings or conventional slotted rings, and error recovery would be more difficult. Hence, its main value is that it serves as an indicator of the maximum achievable performance of the slotted format for local computer network rings operating at low to medium utilization levels under fully distributed access control.>

The commenter points out an error made in the paper by P. Nain et al. (see ibid., vol.36, p.806-15, July 1988) and shows that correcting this error requires a state space that has an exponential size. The authors reply that the errors cannot easily be corrected and that their model provides a good approximation model for HYMAP.>

Z-NET, a high-speed local area network scheme using a dual unidirectional bus architecture and an optical-fiber transmission medium, is introduced. At very light loads, the protocol behavior is similar to a random-access scheme with zero packet delay. As load increases, the operation is similar to an implicit token-passing scheme and ensures fair round-robin scheduling among stations. The protocol is completely distributed, with no station performing any special function. When collisions occur, one station always continues its transmission to completion. Thus, the wastage of bandwidth caused by collisions is minimized. This is achieved using active and passive switches on the buses. Delay performance of Z-NET is superior to Expressnet. Fasnet and tokenless protocols at very light loads. At heavy load, performance is similar to that of Expressnet, with bounded packet delay. No special mechanisms are used to start new rounds or for transition between random and controlled access modes of transmission. Thus, the proposed scheme has the added advantage of simplicity.>

CSMA/CD (carrier-sense multiple-access with collision detection) protocols with two persistence factors is presented. One factor is used to define the protocol action if a station becomes ready when the channel is busy, and the other one is the normal persistence factor used to determine transmission rights. A family of new protocols is described which corresponds to different values of the persistence factors. The performance model of this protocol also acts as a unified performance model for the slotted CSMA/CD protocol with finite population. The non-persistent, 1-persistent and p-persistent variants of the CSMA/CD protocol can all be modeled as special cases of this general model. In addition to average performance measures, the output process and the packet delay time distributions are evaluated.>

X-Net is a new local area network based on the dual, unidirectional bus topology. Stations are connected to two fiber-optic busses using active switches. At light load, X-Net behaves as a random access scheme. With increasing load, transmissions are done in an orderly manner, arranged into cycles.

In this paper we study local area networks based on the star topology. We consider different access protocols used for communication over star networks. The study is concerned with network performance. We present models for the analysis of existing star network protocols. We also propose a new access protocol for star networks. The protocol has a performance that is very close to perfect scheduling. It is based on using the semaphore mechanism for bandwidth sharing. We also present an exact performance model for this protocol.