Integrated Sensing and Communications (ISAC) enhances traditional mobile network capabilities by enabling the detection of passive, non-connected objects. Latency-sensitive vehicular applications such as Augmented Reality (AR), Virtual Reality (VR), and High Definition Maps (HD Maps) can be integrated with ISAC to improve the utilization of limited wireless resources. The Configuration Grant (CG) mechanism, in 3GPP Release 16, reduces signaling overhead in Uplink (UL) by preassigning resources to UEs (vehicles). However, employing CG for ISAC can lead to incorrect assignment of transmission slots due to the aperiodic nature of ISAC sensing traffic. To address this issue, we propose a CG allocation scheme that models the interarrival times of aperiodic traffic using a Weibull distribution. A probability distribution model, implemented and evaluated using the NS-3 5G-LENA CG module, assists the radio resource scheduler by analyzing sensing arrivals within a configuration window to predict future bursts and proactively reserve UL resources for UEs (vehicles). This prediction-driven allocation significantly improves Packet Delivery Ratio (PDR) and spectral efficiency in vehicular scenarios.

Emerging latency-critical applications such as cloud gaming and industrial automation demand agile and ultra-low-latency service delivery, which traditional network appliances struggle to support. Network Function Virtualization (NFV) addresses this by chaining Virtual Network Functions (VNFs) into Service Function Chains (SFCs). Parallelized SFCs (PSFCs) reduce service delay by executing independent VNFs concurrently, but introduce significant copy/merge and buffering overheads due to synchronization delays across branches. Moreover, dynamic PSFC arrival rates complicate efficient VNF placement decisions. This paper presents RL-PARETO, a hybrid deep reinforcement learning approach that adaptively orchestrates parallel VNFs while minimizing parallelization overheads and satisfying SLA constraints. RL-PARETO uses a graph transformer encoder with dual pointer-network heads to jointly generate PSFC partitions and VNF placements in a single pass. Training integrates Proximal Policy Optimization (PPO) for stable exploration with a Double-DQN critic for efficient value estimation. A fallback heuristic ensures feasible deployments under resource constraints. Extensive evaluations across diverse network topologies demonstrate that RL-PARETO achieves up to $10 \%$ higher acceptance rate and $15 \%$ reduction in merge buffer overhead, while maintaining robust performance under dynamic conditions.

In this paper, we address the problem of reconfiguration of network slices (NSs) to handle network node failures and mitigate service interruptions while satisfying the diverse service requirements of users and multiple industry verticals in beyond 5G and 6G networks. First, we formulate the latency- and reliability-aware NS reconfiguration problem as an integer linear programming (ILP) to minimize resource consumption. Then, to deal with the computational time complexity of ILP, we propose a matching algorithm-based heuristic solution. Through extensive simulations, we show that our proposed algorithm provides a nearoptimal solution in polynomial time with an optimality gap of 2.5% and consumes fewer resources while satisfying stringent latency and reliability service requirements.

In dynamic hotspot scenarios, unserved users from the hotspots of the overloaded gNBs can be offloaded to the neighbouring underloaded gNBs by using Unmanned Aerial Vehicles (UAVs) as relays. However, it may lead to co-channel interference as the same frequencies are used by every gNB. Therefore, unlicensed bands are leveraged for communication between the offloaded users and the UAVs in the state-of-the-art. It leads to contention with other users that use unlicensed bands and reduces the efficiency of spectral usage. To avoid the usage of unlicensed bands, we propose an offloading solution where Successive Interference Cancellation (SIC) is used to handle the co-channel interference. The proposed solution can be used for both cell-centred and cell-edge hotspot scenarios. New challenges arise when a multi-gNB and multi-hotspot scenario is considered, such as determining the load re-distribution strategy, determining the number and locations of the UAVs, detecting the hotspots across the coverage area of multiple gNBs, and determining the user offloading strategy that makes SIC feasible. Therefore, we formulate the problem for multi-gNB and multi-hotspot scenario to minimize the total cost of Service Level Agreement (SLA) violations, by jointly optimizing the load redistribution, number and locations of the UAVs, power levels of the gNBs and UAVs, bandwidth allocation and user offloading strategy. We also propose an efficient heuristic solution to the formulated problem and demonstrate its effectiveness through extensive numerical simulations. The numerical simulations show that the proposed solution achieves up to 55.39% fewer SLA violations and up to 37.91% less total SLA violation cost compared to the baseline.

Enabling widespread adoption of resource-intensive vehicular applications such as Extended Reality (XR) and High Definition map (HD Map) necessitates further enhancements in 5G, which is anticipated with 5G-Advanced. These applications, sensitive to latency, prompt researchers to propose offloading vehicles' complex computations to nearby edge clouds, aiming to minimize latency and meeting the Quality-of-Service (QoS) demands of these applications. However, the uncertainties arising from spatio-temporal factors due to vehicle mobility and the dynamic nature of application behaviour pose significant challenges in deciding the efficient offloading decision for minimizing latency. To tackle this challenge, this paper introduces a crosslayer framework that bridges the Radio Access Network (RAN) scheduler with the Mobile Edge Computing (MEC) scheduler. The proposed framework facilitates the exchange of vehicle ranks and channel condition information between schedulers, strategically aimed at reducing Head-Of-Line (HOL) delay for efficient computational offloading. Furthermore, the MAC layer incorporates the prediction of the Buffer Status Report (BSR) using Machine Learning (ML) to further reduce the queuing delay experienced by the offloading jobs of the vehicles in uplink. Simulation results using the NS-3 gym demonstrate that the proposed cross-layer framework achieves a higher Offloading Success Rate (OSR) than the state-of-the-art QoS scheduler by effectively reducing HOL delay for HD Map vehicular application.

New Radio (NR) Sidelink was first introduced by 3GPP in Rel. 16 to meet service requirements of advanced Vehicle-to-Everything (V2X) use cases. The mode-2 of NR-V2X enables direct vehicle-to-vehicle communication through distributed resource scheduling, specifically Semi-Persistent Scheduling (SPS). The focus of Intelligent Transport Systems (ITS) till now has been predominantly on ground vehicles and infrastructure, however the anticipated future scenario where airspace becomes as crowded as ground space by the mid-2030s necessitates a robust and efficient cooperation between aerial vehicles (AVs) and terrestrial/ground vehicles (GVs). There is also an ongoing discussion in the 3GPP Technical Specification Group Radio Access Network (TSG RAN) on supporting aerial communications in AVs by using NR-sidelink. This paper is the first to study extensively the performance of Cooperative Awareness Messages (CAM) and Decentralized Environmental Notification Messages (DENM) in a unified setup, involving systems of aerial vehicles (AVs) and systems of ground vehicles (GVs) in mode-2 using NR Sidelink. The SPS at Medium Access Control (MAC) layer schedules CAM and DENM packets. We conduct a comparative analysis of CAM performance for AVs versus GVs using a 3D geographical dataset of Washington DC, evaluating Packet Reception Ratio (PRR) and Packet Inter-Reception (PIR). Furthermore, we enhance the reachability of DENM among GVs by integrating AVs using the NR sidelink UE (User Equipment)-to-UE relay feature introduced in Rel. 17/18. Finally, we propose a congestion-aware multi-hop DENM dissemination protocol with a novel relay selection mechanism to keep congestion below the maximum Channel Busy Ratio (CBR) limit, thereby improving reception node coverage by 17% compared to state-of-the-art NR-V2X DENM dissemination mechanisms.

The private 5G-and-beyond networks have the ability to fulfill the high data rate, low latency, and massive connectivity requirements of industrial IoT (IIoT) automation. As a result, the slice selection requests arriving at the control plane functions (CPFs) of the private 5G-and-beyond networks will increase. The network has to scale up its resources to improve the response time for slice selection requests. The scaling up of resources will lead to an increase in operational expenditure (OPEX). We propose a novel approach to the slice selection method in private 5G-and-beyond networks for industrial IoT that improves response time for slice selection requests and reduces OPEX for the networks. The response time of our proposed method (protocol) for$u$IIoT devices is approximately$\frac{1}{u}$times that of the traditional slice selection protocol, and the number of instances of CPFs required for the proposed protocol is also lesser than that of the slice selection protocol in 5Gs networks.

G networks can offer network slices customized according to the demands of the services to enhance the quality of their users’ experience. The time for selecting an appropriate network slice to facilitate traffic flow between users and services by the core network functions in 5G networks is crucial for services such as emergency service and ultra-reliable low latency services. Therefore, we propose a distributed slice selection architecture for 5G and beyond networks to reduce the waiting time for starting services for users. The proposed architecture distributes slice selection function (SSF) to the edge of the network. The networks have to ensure stealthy slice selection attack (S3 attack) free operation, as moving the SSF to the edge increases attack surface. Attackers can launch S3 attack by manipulating the slice selection decisions of SSFs distributed in the network edge. The S3 attacker intentionally maps service requests from users to inappropriate network slices to damage cloud radio access network utilization and the quality of experience of users. In this article, we also present a countermeasure to tackle the S3 attack using a novel protocol called VACCINE ( v erifi a ble priva c y-preserving proto c ol for sl i ce selectio n in 5G and beyond n e tworks). VACCINE also ensures privacy-preserving slice selection by the SSFs to prevent traffic analysis attacks. We prove the chosen-ciphertext attack security strength of VACCINE and also compare the computational cost of VACCINE with other related protocols.

Vehicular applications such as Augmented Reality (AR), Virtual Reality (VR), and High Definition Map (HD Map) are known for their latency-sensitive traits. But, dynamic scheduling at the MAC layer incurs significant signalling overhead (in terms of Scheduling Requests (SRs) in Uplink (UL)), leading to non-negligible latency in 5G NR. To address this issue, 5G NR introduces Configuration Grant (CG) for UL transmission, which pre-allocates radio resources to UEs (vehicles), thereby reducing signalling overhead between a vehicle and the Base Station (gNB). However, the high-speed mobility of vehicles results in rapid changes in channel conditions. Employing CG in a vehicular scenario can lead to incorrect assignment of transmission parameters (e.g., Modulation and Coding Scheme (MCS)), thereby adversely impacting the vehicles’ Packet Delivery Ratio (PDR). To address this issue, this paper proposes a CG allocation algorithm that utilizes a Machine Learning (ML)-driven approach to predict the future MCS of vehicles. A data-driven ML model, derived from a real-world dataset, assists the radio resource scheduler and is evaluated using the NS-3 5G-LENA CG module. The ML-assisted CG allocation algorithm demonstrates significant improvements in terms of PDR and spectrum usage efficiency in vehicular scenarios.

By bringing the processing and storage capabilities of the cloud closer to the end devices, fog computing (FC) enhances the Quality of Service (QoS) for latency-critical Internet of Things (IoT) applications, such as autonomous driving, haptics, and augmented reality (AR). To facilitate the processing and storage of data packets, the fog nodes in the underlying FC-enabled IoT network (FC-IoTN) are to be provisioned with storage and processing resources. Existing resource provisioning solutions focus mainly on latency sensitivity and cost efficiency. They also operate under the assumption that these fog nodes are completely reliable and energy efficient. In reality, this is not true. The fog nodes are not 100% reliable. Neither are they energy efficient. In this study, we propose a novel resource provisioning framework for the fog nodes that considers reliability and energy efficiency, in addition to latency sensitivity and cost efficiency. We first give an analytical framework to model the failures and recoveries in a fog node and use this modeling to provision resources in the fog nodes such that the resultant resource provisioning is optimal in terms of cost and energy consumption. Further, to understand the effect of latency, reliability, cost, and energy on resource provisioning, we analyze and decode the interplay between these factors during resource provisioning in fog nodes. We finally show the efficacy of our approach over the scenario that does not consider reliability and energy efficiency while provisioning resources. Without affecting the latency sensitivity and reliability of the system, our framework achieves an enhancement of 35%, and 37% in terms of cost and energy consumption, respectively, over a nonoptimized framework.

Using Unmanned Aerial Vehicles (UAVs) is a promising solution to perform cellular user offloading in dynamic hotspot scenarios. The users from overloaded gNodeB (gNB) can be offloaded to a neighbouring underloaded gNB using UAVs as relays. In this paper, we propose a novel UAV-aided cellular user offloading that uses Successive Interference Cancellation (SIC) for handling interference. Our proposed solution can be used for dynamic hotspots that occur in both cell-centered and cell-edge areas. We do not use any unlicensed band for communication between users and UAV, thereby avoid contention between cellular technologies and technologies that use unlicensed bands such as WiFi. As the users perform SIC for retrieving the signal, user association, location of UAV, and power levels of UAV and gNB are crucial for providing the promised data rates to the users, so that the Service Level Agreements (SLAs) are not violated. Therefore, we formulate the problem to minimize the SLA violations by jointly optimizing the location of UAV, power levels of gNB and UAV, bandwidth allocation and user association. Further, we propose a solution to the formulated problem and show the effectiveness of our proposed solution using extensive numerical simulations. The proposed solution is shown to significantly decrease the total cost of SLA violations and increase the total available rate for the users of the overloaded gNB.

Network Functions Virtualization (NFV) allows flexibility, scalability, agility, and easy manageability of networks by leveraging the features of virtualization and cloud computing technologies. However, softwarization of network functions imposes many challenges. Reliability and latency are major challenges in NFV-enabled 5G networks that can lead to customer dissatisfaction and revenue loss. In general, redundancy is used to improve the reliability of communication services. However, redundancy requires the same amount of additional resources and thus increases cost. In this article, we address the reliability-aware, delay guaranteed, and resource efficient Service Function Chain (SFC) placement problem in softwarized 5G networks. First, we propose a novel SFC subchaining method to enhance the reliability of an SFC without backups. If reliability requirement is not met after subchaining method, we add backups to VNFs to meet the reliability requirement. Then, we formulate the reliable SFC placement problem as an Integer Linear Programming (ILP) problem in order to solve it optimally. Owing to high computational complexity of the ILP problem for solving large input instances, we propose a modified stable matching algorithm to provide near-optimal solution in polynomial time. By extensive simulations we show that our proposed solutions consume lesser physical resources compared to state-of-the-art solutions for provisioning reliable communication services.

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years.

To meet the ever increasing data rate demand in cellular network, the 3rd Generation Partnership Project (3GPP) introduced Licensed Assisted Access (LAA) with Listen Before Talk (LBT) mechanism to utilize the unlicensed spectrum to supplement the licensed LTE users. However, this leads to interference to already incumbent technologies in unlicensed spectrum such as WiFi. Furthermore, multi-carrier aggregation has been proposed to further increase the capacity in unlicensed spectrum. As WiFi primarily works on the principle of energy detection in the channel, in this paper, we first develop an analytical model to measure the performance of LAA and WiFi coexistence in unlicensed spectrum which supports multi-carrier aggregation which employs SINR based collision that allows the possibility of concurrent transmissions. Further, to reduce the interference to WiFi, we propose a coexistence method named Carrier Aggregation with Transmit Power Control (CATPC) which dynamically adjusts the LAA transmit power. CATPC provides a better opportunity for WiFi devices, better channel utilization, and energy efficient transmission by LAA. Through extensive simulations we demonstrate the effectiveness of CATPC when compared to other coexistence methods proposed in the literature.

The proliferation of sensors as part of the Internet-of-Things (IoT) paradigm envisions a world of interconnected, interdependent deployments, involving one or multiple radio technologies. Multiradio mesh deployments facilitated using a network of low-cost IoT platforms, such as Raspberry Pis (RPis), ASUS Tinker Board S, Banana Pi M64, etc., seem an ideal fit as wireless backbones for such deployments, with many of these platforms having built-in support for multiple wireless technologies. However, the state-of-the-art routing solutions need to be revisited to suit the capabilities of low-power multiradio solutions, especially while performing best effort transmissions. This is because a majority of the existing routing solutions favor channel diversity while routing, ignoring the possible overheads associated with protocol conversions. We argue that in reality, these overheads can become quite significant in a multiradio network where the radios are quite different in terms of packet formats, sizes, data rates, etc. To prove this, we estimate the impact of these overheads using an experimental study performed across three popular IoT technologies-WiFi, Bluetooth, and ZigBee, using a real-time RPi-based testbed. We stress on how severe this impact could be, by estimating how often these conversions happen in a randomly chosen multihop path, using a probabilistic mathematical framework. Based on this, we develop Chorus, a first-of-its-kind routing algorithm, which estimates the least overhead path, inclusive of conversion overheads. Chorus achieves this by developing a comprehensive layered graph modeling of the given multiradio mesh network. The modeling is capable of handling all conversion overheads, including the factors associated with the possible fragmentation and aggregation. We implement Chorus in an indoor testbed comprising of RPis and demonstrate the performance improvement while routing across the network of RPis. Our results estimate that Chorus can provide an improvement close to 33% in battery usage per RPi with an average reduction of 25% in packet drops.

In the softwarized fifth generation (5G) networks, authentication protocols are deployed by both the mobile network operators and the third-party service providers to enable secure slice formation at the network side and user validation at the service provider side, respectively. However, the usage of static key-based authentication protocols in 5G networks gives rise to two major problems: (i) network slice topology learning attack during the formation of slice and (ii) exposure of users’ service access behavior to the third-party service providers and device-to-device communication peers. In this article, we propose group anonymous (i) privacy preserving mutual authentication (PPMA) and (ii) privacy preserving one-way authentication (PPOA) protocols to address the aforementioned problems, respectively. We also prove the security of the PPMA and PPOA protocols against the chosen ciphertext attack in the random oracle model. The PPMA and PPOA protocols are implemented using the JPBC library and the computation overhead is measured. For 1024 bits discrete logarithm security, the computation overhead of the PPMA and PPOA protocols is respectively 48.54 and 68.65 percent lower than an existing privacy preserving authentication protocol on an Intel processor. The PPMA and PPOA protocols are generic and can be applied in peer-to-peer authentication scenarios where group anonymity and privacy preservation are considered.

Network slicing and multi-access edge computing (MEC) are new paradigms which play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide the available network resources into multiple logical network slices (NSs) for providing dedicated virtual networks tailored to the specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services for supporting the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber attacks, power outage, and natural/man-made disaster. Failure of a critical network component disrupts services abruptly and leads to users’ dissatisfaction, which may result in revenue loss for the NOs. In this paper, we present a novel approach based on multi-connectivity in 5G networks to tackle this problem and our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility in regional level. To this end, we formulate the problem as a binary integer programming (BIP) model in order to optimally deploy NSs with the minimum cost, and prove it is NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we propose an efficient genetic algorithm based heuristic to obtain near-optimal solution in polynomial time. By extensive simulations, we show that our proposed approach not only reduces resource wastage, but also improves throughput while providing high resiliency against failures.

Network Functions Virtualization (NFV) allows softwarization of network functions and enables to run network functions as Virtual Network Function (VNF) instances on top of the cloud infrastructure. In softwarized 5G networks, communication services can be realized through Service Function Chains (SFCs) in which multiple VNFs are connected in a sequential order as per the requirements, which offer flexibility, agility, and dynamic management and orchestration of networks. However, softwarization and cloudification of networks using NFV introduce new challenges in terms of reliability and availability due to software bugs, hardware failures, malfunction of VNFs, and service degradation. In this article, we first explore latency-aware and reliable SFC placement to meet the requirements of users and enhance the reliability of SFCs from VNF failures. Then, we focus on reliable placement of virtual monitoring functions at proximal locations of VNFs in order to identify and mitigate service degradation and security-related issues in the network. To this end, we formulate the problems as Integer Linear Programming (ILP) problems to minimize the total deployment cost, and show that they are NP-hard. To overcome the high computational complexity of the ILP problems, we propose novel heuristic algorithms based on complex network theory to provide near-optimal solutions in polynomial time for large input instances. By extensive simulations, we show that our proposed algorithms provide near-optimal solution (optimality gap is 5%) in a real-world network topology.

Real-world Machine-to-Machine (M2M) networks comprise of power-constrained sensor nodes to collect data of interest and send it to data aggregators for further processing using multi-hop relaying which increases end-to-end transmission delay. However, in many real-world application scenarios such as low latency communication in a 5G network, there is a strict delay deadline that has to be met. In this paper, we propose Constrained Link Addition Using high Sensor nodes (CLAUSe) to efficiently construct a few long-ranged links among the chosen high sensor nodes (sensor nodes with at least two interfaces) to reduce end-to-end hop distance in a sparse random M2M network setting, thereby, incorporating the small-world characteristics.

Cloud Radio Access Network (C-RAN) is a crucial component in 5G network deployments. The C-RAN technology will reduce operating costs, improve energy efficiency, and support enhanced capacity and coverage by taking advantage of centralized processing. However, C-RAN requires strict latency bounds on the fronthaul network that connects the distributed radio units and the central processing units. Instead of deploying dedicated fronthaul transport networks, the reuse of existing backhaul transport networks to carry fronthaul data needs to be leveraged. In this paper, we study the end-to-end delay bounds for a mixed fronthaul and backhaul 5G network. Deterministic network calculus is used to derive worst-case delay bounds in a mixed transport network, and this is verified using a C-RAN experimental testbed with real traffic characteristics. The impact on end-to-end delay due to the effect of multiplexing fronthaul and diurnal backhaul traffic in business and residential regions is studied analytically. We also propose a framework to determine the choice of enabling the appropriate fronthaul split at the distributed radio units and the central processing units to meet the latency requirements. The proposed network calculus based framework can be utilized by network engineers while designing the future 5G transport network deployments.

Mobile Network Operators perceive Spectrum Slicing and Sharing as an efficient and viable option to meet the growing mobile user data demands in the upcoming 5G network deployments. Efficient spectrum sharing requires optimized spectrum partitioning, i.e., slicing of the spectrum for sharing among operators, to improve spectrum utilization, offer services with high quality of experience, and increase their revenues. This paper proposes Spectrum Slicing Coalition Formation (SSCF) protocol, a novel solution to address effective spectrum slicing required to meet the dynamic data demands at every 5G base station. The base stations' cooperation is formulated as a coalition formation game with overlapping coalitions, where a base station's contribution is not restricted to a single coalition, instead a base station participates intelligently in multiple coalitions. The SSCF protocol enables a base station to dynamically slice and share its allocated spectrum to service its load based spectral requirements, a key requirement for effective radio access network softwarization. The study explores operator base stations' options to determine with whom, when, and how much to slice and share the spectrum and the payoffs. Extensive simulations using real-world operator base station deployments highlight that SSCF solution brings in operational efficiencies, improved users' quality of experience, and revenue maximization.

In order to meet the exponential rise in mobile data demand, it is proposed to supplement existing licensed bandwidth with the unlicensed spectrum, where a part of cellular data demand is served in the unlicensed spectrum. This deployment of LTE technology in unlicensed spectrum is termed as LTE in unlicensed spectrum (LTE-U). However, deploying LTE technology in the unlicensed 2.4/5 GHz band will greatly cause interference to the already existing technologies such as WiFi and Zigbee due to the stark differences in their channel access mechanisms. For LTE-U to become a reality, it is necessary for LTE to fairly coexist with the existing technologies. Therefore, in this paper, we propose a Q-Learning based Dynamic Frame Selection (DFS) algorithm which ensures fair coexistence between LTE-U and WiFi technologies. Furthermore, we propose the use of reduced power subframe to limit interference to the co-channel users and increase the channel utilization. By extensive simulation we show the effectiveness of our proposed DFS algorithm when compared to existing algorithms in the literature.

Cloud Radio Access Network (C-RAN) technology is a key enabler for 5G network deployments. It brings in the benefits of centralized processing to support dense deployments and to reduce the operating costs. The flip side of C-RAN is it places stringent latency and bandwidth requirement on the fronthaul network connecting radio units and central units. In this work, we propose an efficient topology-aware synchronized packet generation scheme using a centralized SDN controller to reduce the overall network congestion. The study also provides an insight into packet-based SDN-enabled C-RAN behavior modeled as a network of queues and is analyzed by an extensive simulation using the traffic characteristics captured from a C-RAN testbed implementation. The analysis provides useful insights for network designers in enabling future C-RAN deployments.

In this paper, we investigate the impact of Resource Spreading on the capacity of mmWave based Device-to-Device (D2D) overlay networks. We aim to improve the spectral efficiency of these networks under low load conditions at Base Station (BS). Our proposed Resource Spreading scheme utilizes the fact that during a certain period of time, the number of active D2D pairs under a BS may be much lower than what is the maximum allowed, and available Resource Blocks (RBs) may be under-utilized. We present a stochastic geometry based framework to analytically evaluate the proposed Resource Spreading scheme for mmWave D2D overlay networks. Extensive simulations and analysis reveal that our proposed Resource Spreading scheme shows an improvement in network performance by allocating RBs efficiently, in terms of both spectral efficiency and energy efficiency.

Slicing of a 5G network by creating virtualized instances of network functions facilitates the support of different service types with varying requirements. The management and orchestration layer identifies the components in the virtualization infrastructure to form an end-to-end slice for an intended service type. The key security challenges for the softwarized 5G networks are, (i) ensuring availability of a centralized controller/orchestrator, (ii) association between legitimate network slice components, and (iii) network slice isolation. To address these challenges, in this paper, we propose a novel implicit mutual authentication and key establishment with group anonymity protocol using proxy re-encryption on elliptic curve. The protocol provides (i) controller independent distributed association between components of a network slice, (ii) implicit authentication between network slice components to allow secure association, (iii) secure key establishment between component pairs for secure slice isolation, and (iv) service group anonymity. The proposed protocol's robustness is validated with necessary security analysis. The computation and bandwidth overheads of the proposed protocol are compared with that of the certificate based protocol, and our proposed protocol has 9.52% less computation overhead and 13.64% less bandwidth overhead for Type A1 pairing.

A smart city aims at providing effective services to its citizens and administrators through the Internet of things (IoT) paradigm. A typical smart city architecture consists of a large topology with heterogeneous data generating sensors and communication technologies with support for multiple applications such as traffic management, smart energy, smart homes, and industrial IoT. Each application has a unique data generation rate and latency constraint based on the criticality of the application. In traditional IoT architecture, the data is processed in the Cloud which results in high propagation delay and hence high latency. To alleviate this problem, a decentralized and distributed paradigm called Fog Computing (FC) is gaining importance in latency critical IoT networks. The Fog layer is an intelligent layer in which the intermediate gateways are smart and have processing and storage resources. Smart processing of data at the fog layer results in significant reduction in latency. However, the smartness of gateways comes with a price. Therefore, placement of these resourceful gateways has to be done in a cost efficient manner considering latency constraints of all the applications supported in the system. In this paper, we propose a Hungarian method based Topology Construction (HTC) algorithm to identify the optimal locations and resources for the gateways such that over all system cost is minimized and latency constraints are met. We perform exhaustive simulations to show the effectiveness of our algorithm by comparing with existing models.

With advancements in mobile communications and technologies, Almost Blank Subframe (ABS) has found a lot of applications. It has been applied in heterogeneous networks to optimize various performance metrics, in LTE Licensed Assisted Access (LTE-LAA) for harmonious coexistence of LTE with WiFi in unlicensed spectrum, in enabling D2D communication in licensed spectrum, and so on. In any scenario, optimizing ABS density is very important in order to maximize the resource utilization, and hence other performance metrics. In this paper, we propose a generalized framework to find optimal ABS density that can be applied in any wireless network and application which balances the shortage and under-utilization of resources at various entities. To the best of our knowledge there does not exist such a framework which can be applied across various wireless networks and applications. We also propose models to apply our framework in heterogeneous networks and LTE-LAA. Through extensive simulations we show that the results obtained using our framework is the optimal solution.

With the rapid increase in mobile data traffic (especially video content traffic), the number of content access requests is also increasing. In this context, device-to-device (D2D) communication has been an effective technology in increasing the spectral efficiency and reducing the load by offloading the mobile data traffic in cellular networks. In order to reduce the load on eNodeBs (eNBs), centralized area controller (CAC) has been proposed earlier to take content aware decisions for content access requests. In this paper, we propose to exploit CAC in a distributed manner. Distributed D2D controller (DDC) is responsible for arbitrating and scheduling D2D transfers. Thus, every content access request from a user equipment (UE) is first served by a neighboring DDC and in case the content is not available in the region, the request is forwarded to the eNB. Further, it has been observed that the inclusion of DDCs in the existing D2D architecture reference model requires the addition of new reference points. The same has been highlighted in this paper. Due to the spatiotemporal correlation in the mobile data traffic, a learning algorithm can be employed that determines the number of required resources to fulfill the D2D data rate requirements. Hence, we propose a Q-learning based algorithm that learns the expected number of resource blocks (RBs) required to meet the data rate requirements at each DDC. In addition, a cache content management policy is proposed that exploits the popularity of the contents in order to increase the chances of D2D communication. With extensive simulations, we observe that the proposed Q-learning algorithm indeed learns by reserving near-optimal number of RBs to serve the data rate requirement at each DDC. Further, we observe that the one- and two-hop modes of D2D transfer effectively reduces the load on eNB by transferring a maximum 49% of the required data to the UEs.

Internet of Things (IoT) in a real world scenario involves collecting data by sensor devices and communicating the sensed data, through gateway devices, to the IoT Cloud (or the Internet) using direct or multi-hop communication. A gateway typically supports multiple wireless sensor networks (WSNs) and each WSN might use a different technology for communication. Hence gateways are designed to be heterogeneous, having necessary hardware support only for those technologies corresponding to their associated WSNs. Due to their heterogeneity, the technology specific channel access mechanisms need not provide fairness in data communication at each gateway. In this paper, we propose two centralized techniques that involve an efficient link selection per technology and time based scheduling among gateways. This improves fairness in data transmission for every gateway, resulting in an increased data transmission. The first technique uses a multinomial probability based link selection thereby reducing contention within each technology link for a given network topology. The second technique incorporates adaptation to the changing traffic patterns in the network. Our extensive simulation results show that the proposed techniques outperform default technology specific channel access mechanism in IoT networks in terms of total data transmitted, packet delivery ratio, and fairness.

With the exponential growth in mobile data traffic, the number of content access requests is increasing and hence, the load on the LTE eNodeB (eNB) is also increasing. In such a scenario, transferring data files using Device- to-Device (D2D) communication helps in offloading cellular data traffic. In order to reduce the load on eNB, Centralized Area Controller (CAC) has been proposed earlier to take content aware decisions for content access requests. CAC uses long range LTE communication for uplink transmission thereby leading to more energy consumption. Additionally, it relies on eNBs for spectral resource allocation thereby increasing the load on the eNBs. In this paper, a Distributed D2D Controller (DDC) deployment for arbitrating and scheduling D2D transfers is proposed. Every content access request from a User Equipment (UE) is first served by a neighboring DDC. In case the content is not available in the region, the request is sent to the eNB. A complete DDC working protocol that explains the above procedure is proposed. The service rate requirement for the DDC to achieve the same expected response time as that of the CAC is calculated. In order to reduce the overall cost of placement of DDCs, a DDC placement algorithm that uses convex hull to determine the locations for placing DDCs is proposed. The performance of DDCs is compared with CAC in terms of the number of content access requests that are serviced using D2D. In addition, we compare the total power consumed in uplink transmission in the case of DDC and CAC. Extensive simulations demonstrate that our proposed distributed controller is more energy efficient from users perspective than that of the centralized controller.

The future 5G cellular networks are expected to support several-fold increase in data traffic and number of devices, and provide a very low latency and gigabit-rate data services. millimeter Wave (mmWave) communication (30–300 GHz) is proposed to be an important part of the 5G cellular networks to fulfill these requirements. With most of the high data rate demands originating from indoor User Equipments (UEs), conventional wired backhaul links prove to be a major bottleneck. Hence, mmWave wireless backhaul links are explored at Femto Base Stations (FBSs). In order to derive maximum benefit of mmWave backhaul links, efficient resource (data slots) utilization is necessary. Proper scheduling of mmWave backhaul links will help to achieve the same. In this paper, we propose a novel FBS-to-FBS (F2F) communication scheme which not only helps in offloading the data traffic from the Micro Base Station (MiBS) but also can act as a relay link for the other FBSs for routing the backhaul traffic to the MiBS. We also propose a solution to concurrently schedule mmWave wireless backhaul links to increase the resource utilization considering the coexistence of both FBS-to-MiBS (F2M) and F2F links. Our proposed solution consists of two parts — first, it determines which backhaul links can be scheduled concurrently by finding the upper bound for the interfering distance based on the alignment of different lobes of the directional antennas. Second, the transmit power is throttled to match the Quality of Service (QoS) requirements of each link aiming to further control the interference and to increase the number of concurrent transmissions. The benefits of the proposed solution are studied and compared with the Time Division Multiple Access (TDMA) and random scheduling schemes using extensive simulations.

With a wide range of applications, Machine-to-Machine (M2M) communication has become an emerging technology for connecting generic machines to the Internet. To ensure ubiquity in connections across all machines, it is necessary to have a standard infrastructure, such as 3GPP LTE-A network infrastructure, that facilitates such type of communications. However, owing to the huge scale of machines to be deployed in near future and the nature of data transactions, ensuring ubiquitous connections among all the machines will be difficult. Solutions that not only maintain connectivity but also route machine data in a cost effective manner are the need of the hour. In this context, it has been suggested that Device-to-Device (D2D) communication can play a very important role in expanding network coverage and routing the data between source-destination machine pairs. In this paper, we conduct a feasibility study to highlight the impact of multi-hop D2D communication in increasing the network coverage and average rate of a Machine Type Communication (MTC) device. We present a stochastic geometry based framework to analyze the coverage probability and average data rate of a three-hop M2M network deployed along with User Equipments (UEs) and conduct extensive simulations to study the system performance. Our simulation results show that the three-hop M2M network formed from out-of-range MTC devices and UEs can significantly improve the coverage and average rate of the entire network. Due to the mobility of users in the network, design of robust routing mechanisms in such a time evolving network becomes difficult. Hence, we suggest the use of space-time graph built from the predicted user locations to design a cost efficient multi-hop D2D topology that enables routing of MTC data to its destination.

Internet of Things (IoT) is a novel communication paradigm where devices, identified by unique addresses and belonging to different networks, are connected to the Internet. Communication between these devices and the Internet is facilitated with the help of IoT gateways. To have maximum coverage of network with reduced overall deployment cost, optimal placement of gateways is necessary. Owing to the high cost involved with IoT gateways, devices known as Solution Specific Gateways can be utilized along with IoT gateways for cost-effective gateway placement. In this paper, we propose a Network Intersection based Candidate Gateway Location Selection algorithm called NewIoTGateway-Select which determines the minimum number of gateways to be placed in a given Smart City scenario. NewIoTGateway-Select algorithm computes the candidate locations for gateway placement and selects optimal locations from them, in polynomial time. Additionally, our algorithm takes care of gateway failures and link failures by using k-coverage-connectivity based fault tolerance scheme. Our simulation results show that NewIoTGateway-Select algorithm gives better performance than Grid based Candidate Location Selection (GCLS) and Default Gateway Assignment strategy.

Internet of Things (IoT) is considered to be the next revolution in the field of wireless communications. This concept involves mapping of physical world to virtual (cyber) world. It is achieved by interconnecting devices having sensing capabilities using unique addressing scheme and passing their collective information to the IoT cloud. However at present, these devices belonging to different technologies are connected to the Internet using IoT gateways. With the upcoming of a large number of IoT devices connecting to the Internet, there exists a requirement of deploying a large number of IoT gateways in the network which is not cost effective. Therefore, in this paper, we make use of low cost Solution Specific GateWays (SSGWs). As voluminous data is generated by these IoT devices, routing in the IoT network becomes a challenge. In this paper, we propose parallel opportunistic routing in the IoT network using varied kinds of wireless technologies used by SSGWs. The simulation results show that our proposed algorithm performs better in terms of average network throughput and average packet delay when compared to normal routing using single technology.

As the mobile data traffic continues to grow rapidly, spectrum availability is a major concern. Furthermore, the fixed spectrum allocation to the mobile network operators (MNOs) and lack of free spectrum result in poor user quality of experience (QoE) and inefficient spectral resource utilization. Inter-operator spectrum sharing is a solution to overcome the spectral shortage, and this is achieved either by an MNO leasing other MNO's spectrum or by sharing a common pool of MNOs' spectrum. Although both approaches bring benefits, they have further challenges. We propose a novel spectrum sharing paradigm called inter-operator proximal spectrum sharing (IOPSS), where a base station (BS) intelligently offloads users to the neighboring BSs based on spectral proximity to enhance the users' QoE and spectral resource utilization. Users requesting high service rates can be served by using carrier aggregation. We demonstrate the IOPSS's benefits using a continuous-time Markov chain-based analytical model of a BS. A generic IOPSS Q-learning framework (IOPSS-QLF) for a BS to dynamically determine its load-based spectral needs and efficiently share its spectrum resulting in a self-organizing spectrally efficient network of BSs is proposed. The effectiveness of IOPSS-QLF is demonstrated using extensive simulations.

With the evolution of various new types of application services for mobile devices, cellular operators have started providing multiple subscription plans to the mobile users. The plan subscribed determines the Quality of Service (QoS) to be provided to the user, and operators distinguish users as priority users and non-priority users, accordingly. To ensure better QoS to the priority users, necessary resources must be reserved at the base station. This demands analyzing the network traffic to predict future traffic pattern. This paper pioneers the use of machine learning methods to forecast mobile user traffic pattern for providing better QoS to the priority users. We analyze two different supervised learning methods, Naive Bayes Classifier and Logistic Regression, used for prediction of probable times when a priority user would be active. The prediction results are applied to the user scheduling strategies for efficient bandwidth management, thus improving system capacity and reduce blocking. Simulations on multiple real-life datasets validate the model and predict the mobile user pattern with very high accuracy, along with significant reduction in priority user service blocking ratio and improvement in their capacity.

Effective ways to address spectrum shortage by sharing cellular spectrum among the operators are constantly explored. Inter-Operator spectrum sharing, where operators dynamically share their licensed spectrum, when the network capacity requirement varies based on user access is an option to address this issue. The emerging licensed Co-Primary spectrum sharing, a commonly shared spectrum band used among a set of operators is an alternative option. Both these options require the supporting network infrastructure to adapt dynamically to the different bandwidth processing requirements, and this is a challenge. Adapting the proximity based access prevalent in the financial networks, which enhances customer's Quality of Experience (QoE), this study explores the benefits of exploiting the proximal based access in cellular networks. Proximal Spectrum Access (PSA) enables a user to access the nearest base station irrespective of his/her service provider association, which enhances the user's QoE and lessens the strain on network resources. With extensive simulations based on real life multiple operator deployment scenarios, this study validates the deployment benefits to operators as well as end user QoE enhancements.

Device-to-Device (D2D) communication as an underlaying cellular network empowers direct communication between two users and helps in offloading network traffic. However, due to interference posed by the D2D transmitters to the primary cellular users, intelligent resource allocation techniques need to be incorporated for the betterment of the overall system. Overlay D2D communication eliminates the interference between cellular users and D2D users by dedicating resources for D2D users. With this approach, interference among D2D users can still pose a potential threat to the overall system performance. To handle this additional interference without significantly affecting the spectrum efficiency, smart assignment of wireless resources is necessary among D2D users. For the first time, we look into this problem and propose an efficient channel assignment scheme that mitigates the interference among D2D users. By meticulously assigning the channels with proper power control, the proposed channel assignment scheme is found to improve the spectrum reuse as well as the energy efficiency of the system. We compare our scheme with Full Reuse (FR) channel assignment scheme where every D2D pair fully shares the channel resources. Extensive simulations demonstrates that our scheme performs better than FR in terms of spectrum reuse, energy efficiency and overall system blocking.

Use of femtocells for indoor and office environment has proved to be an effective solution to handle ever increasing mobile data demands. Femtocell helps improving network capacity in an energy efficient manner without significantly burdening the operator with huge capital and operational expenditure. Since extremely dense femtocell deployments are expected in near future, it is of interest to look into their energy efficiency measures. In this paper, we analyse the energy efficiency aspect of cell selection scheme for femtocell networks. Cell selection scheme defines the criteria on which mobile users associate themselves with base stations. Hence, it plays a crucial role in system load balancing and total energy consumption. We suggest a unique cell selection scheme that assigns mobile users to femtocell base stations considering the capacity improvement obtained per unit increase in transmit power. Our proposed scheme shows an improvement in network performance in terms of both system capacity and energy efficiency. Additionally, we suggest the use of power spreading over subchannels to keep overall interference minimum while maximizing spectrum utilization.

Densely deployed small cell base stations are a key factor to enable 5G networks. Multicore System-on-a-Chip (MCSoC) solutions supporting small cell LTE base station functionality are commercially available. An MCSoC has to be integrated with necessary software components leveraging specific accelerators for creating a complete LTE base station. System architects have a tough challenge in determining whether a small cell MCSoC would meet their intended small cell base station's feature, functionality and performance demands. This paper discusses the application of modular performance analysis (MPA) using Real-Time Calculus (RTC) on an MCSoC-based LTE base station implementation. Our study validates its applicability as well as its ability to identify new designs that will improve the system performance. This study is first in its kind to propose an analytical framework for analyzing an LTE base station's system design and performance.

Many-to-Many (M2M) communication, which allows a group of nodes to communicate with each other simultaneously, has been shown to help battle contention for scarce resources in Delay Tolerant Networks (DTNs), and hence improve their performance. In this paper, we address for the first time the problem of joint message scheduling and drop in a DTN which employs M2M communication. We develop a generic framework, assuming the routing algorithm provides us with a utility function (derived in order to optimize some desired metric) which helps us compute utilities associated with each message. The goal of our joint message scheduling and drop framework is to maximize the increase in total utility. We formulate the problem of joint message scheduling and drop in DTNs that employ M2M communication as an integer optimization problem and show how to reduce it into a number of many-to-one problems. In order to solve the many-to-one integer optimization problems obtained, we develop an efficient but 1/2 -approximate greedy algorithm (Almost-Greedy), and use it to develop an optimal solution (DynOpt). Finally, we use DynOpt to develop an (1 - ε)- approximate Fully Polynomial Time Approximation Scheme (eOpt) that lets us choose a tradeoffs between complexity and accuracy. The problem of joint message scheduling and drop in DTNs employing the traditional one-to-one communication is a special case of our problem where the communicating group size is limited to 2 and hence our contribution provides a theoretical formulation and theoretically proven solutions for that problem too. We compare our algorithms against adaptations of the state of the art one-to-one scheduling and drop policies to the M2M communication case using simulations and find that our algorithms significantly improve the performance of the network.

Femtocells have proved to be an effective solution for handling the ever increasing demands for wireless data without incurring additional deployment costs. Deployment of these low cost, miniature base stations not only improves network robustness but also facilitates efficient location specific services for mobile users. However, dense deployment of femtocells comes with the cost of increased interference. To handle this additional interference without significantly affecting spectrum efficiency, smart assignment of wireless resources is necessary among femtocells. In this paper, we suggest a technique to intelligently reuse the available wireless resources among interfering femtocells so as to improve spectrum reuse and energy efficiency. Additionally, the suggested technique also shows improvement in system blocking for all possible deployment scenarios. Obtained results are verified using extensive simulations.

Road-Side Units (RSUs) act as a backbone for Vehicular Ad hoc Networks (VANETs). The location and density of RSUs largely affect the performance of VANETs. Although, large number of RSUs guarantee better coverage and performance, it results in high capital and operational expenditure. Hence, optimal placement of RSUs is considered as an important problem in VANETs. In this paper, we propose a novel approach to solve the optimal RSU placement problem using Analytic Hierarchy Process (AHP). Our evaluations show that AHP requires lesser number of RSU deployment which eventually leads to lower overall cost. We also analyze coverage achieved by defining expected RSU-to-RSU delay as a performance metric. We find that our AHP based solution consistently outperforms in terms of delay compared to uniform and hot spot placement strategies for various vehicle densities. Obtained results are verified using extensive simulation.

With the emerging demand for vehicular safety and comfort, research in vehicular ad hoc networks (VANETs) has received more importance lately. Road side units (RSUs) being a key element for communication in VANETs, optimal placement of RSUs has become a challenge to ensure ubiquitous connectivity and lower deployment cost. With the emphasis on minimizing carbon footprint, energy aware strategies in placement are necessary. A direction orthogonal to it is sleep scheduling of RSUs to minimize their energy consumption. Taking these into account, we aim to perform optimal placement of RSUs with sleep scheduling where RSUs are powered by conventional grid and solar power. This is done by jointly optimizing the total number of RSUs deployed, the operational expenditure and the conventional grid energy consumed. We use Rainbow Ranking algorithm to place and schedule RSUs for a given scenario. Our results show that this kind of joint optimization leads to an overall energy aware RSU placement with lower overall cost.

In Heterogeneous cellular networks (HetNets), co-channel interference is a major concern due to the co-existence of multiple base stations with overlaid regions. Edge users are typically the victims because of high interference exposure. To counter this high interference, picocells communicate with the edge users in protected subframes (PSF). The severity of the problem intensifies in case of hotspot deployment, where picocells cannot provide coverage to the entire hotspot, thus forming a dense ring of macrocell users around picocells. We argue that these macrocell users are also victims, constituting a significant victim user population in hotspot deployment. We propose that, along with the macrocell muting during PSF, picocells should also be operated in cooperative manner with macrocell, and be barred from transmission during some of the subframes for protection of these macrocell victim users. We define a utility function to find the optimal values of PSF density for both macrocell and picocells, which would increase victim user throughput thereby enhancing system fairness. Exhaustive simulations illustrate that the proposed scheme improves victim user throughput significantly, while maintaining the overall system capacity.

With the advent of wireless technologies such as Wi-Fi Direct and Near Field Communication (NFC), infrastructure-less Peer-to-Peer (P2P) content sharing among mobile devices is set to become more ubiquitous. Delay-Tolerant Networks (DTNs), with their opportunistic message forwarding strategy, can be leveraged to provide seamless connectivity in such scenarios. To the best of our knowledge, little has been done to understand the performance of DTNs under realistic settings involving the interplay of diverse factors such as bundle fragmentation, scheduling, and buffer spacing. In this paper, we look at Queueing Petri Nets (QPNs) as a modeling framework to study the performance of DTN routing. We develop QPN models for DTNs of increasing complexity in an incremental fashion, starting from a network that employs the rudimentary direct transmission routing protocol to a network that employs a family of multi-hop and replication-based routing protocols (namely (p, q) -epidemic routing). The complete QPN model considers a number of realistic factors that impact performance such as finite buffer space, finite link bandwidth, bundles with different priorities and intra-scheduling delays arising due to different levels of the memory hierarchy at the nodes. We come up with a three-fold validation scheme to assert the veracity of our proposed models, via comparison of results obtained from simulations of the QPN vis-a-vis those obtained from direct simulation of the underlying DTN and experimental results obtained from a testbed of Android based devices that employ a mobility emulation scheme. We also show a case to exemplify the analytical capability of the QPN, by deriving the underlying reachability graph and constructing an equivalent stochastic jump process. We identify the stochastic process to be a Semi-Markov Process (SMP) and hence arrive at a closed form expression for the end-to-end delivery latency by computing the hitting time of the SMP. We find that the model accurately captures the behavior of a DTN in numerous realistic scenarios, showing the efficacy of QPNs as a suitable modeling framework for evaluating the DTN routing protocols.

To improve benefits of macrocell offloading in femto-assisted cellular networks, concept of cell biasing has been proposed. Cell biasing attempts to offload users from macrocell by modifying cell selection criteria. This is done by adding a positive bias to the measured signal from femtocells before performing cell selection. While the macrocell offloaded users may experience lower signal quality from femtocells, they are benefited by receiving higher bandwidth. From users' point of view, it is desirable that user equipments receive highest possible bitrate from the target base station. However, cell biasing only considers received signal strength to make cell selection decisions. The bitrate received at a user equipment is directly proportional to available bandwidth and user load at target base station. In this paper, we propose an enhanced cell selection scheme that considers scheduling opportunities available at femtocell by analyzing the current load and femtocell specific constraints such as maximum user count and minimum signal strength. Obtained results show that our work provides the best performance in terms of both system throughput and energy efficiency among all compared cell selection schemes.

Multiple antenna techniques are widely being recognized as front-runners in improving the performance of a wireless network. Sharing a slot between two Mobile Stations (MSs) is defined in a Point-to-Multipoint (PMP) network, independent of the number of antennas at each MS. However, sharing a slot among more than two MSs in a PMP network is not studied in the literature. In this paper, we study if more than two MSs can share a slot for transmission. We find that there exists few realistic scenarios where more than two MSs can share a slot, thus improving the throughput of the network. We observe that the number of MSs sharing a slot is dependent not only on the number of antennas at each MS but also the multiple antenna technique utilized for transmission. When each MS has a constant rate requirement of 2 bps/Hz, we found that 3 MSs can share a slot when each MS uses Spatial Multiplexing techniques, while 4 MSs can share a slot when each MS uses Spatial Diversity techniques for transmission.

With the advent of wireless technologies such as Wi-Fi Direct and Near Field Communication (NFC), Peer-to-Peer (P2P) content sharing among mobile devices is set to become more ubiquitous. Delay-Tolerant Networks (DTNs)-with their rudimentary direct delivery routing protocol-can be leveraged to provide seamless connectivity in such scenarios. To the best of our knowledge, little has been done to understand the performance of DTNs under realistic settings involving the interplay of diverse factors such as bundle fragmentation, scheduling, and buffer spacing. In this paper, we present a Queueing Petri Net (QPN) abstraction of DTNs that enables us to evaluate the underlying network's performance. Our model is novel in its ability to capture bundle fragmentation, scheduling, and buffer spacing put together. We proceed to evaluate the veracity of the model by involving QPN evaluation using the SimQPN tool and simulation of the underlying DTN using the ONE simulator. We find that the model successfully predicts the performance of the underlying network to a high degree of accuracy.

In Wireless Sensor and Actor Networks (WSANs), effective Actor-Actor Communication (AAC) is an important requirement for the timely responses to events reported by the sensors. However, due to scattered nature of events, mobility of actor nodes, and low density of actor nodes, the network of actor nodes tends to get partitioned frequently. To provide effective AAC in such situations, the energy-constrained sensor nodes located between the partitioned actor nodes need to be utilized. This solution for healing the actor network partitions should involve minimal use of the sensor nodes so that the network lifetime is maximized. In this work, we propose an energy-efficient Actor-Actor Reliable Transport Protocol (A2RT) for WSANs with actor nodes equipped with directional antennas and dual radio interfaces. Our proposed transport protocol consists of a transport wrapper and a dynamic priority scheduler. Using simulations, we show that our transport wrapper achieves high reliability with minimum retransmissions both under static and dynamic network topology conditions. The results also show that the traffic scheduler of our protocol helps to achieve the goals of real-time delivery by maximizing the number of packets that meet the delay constraints.

Actor–actor communication is an important part of the functioning of wireless sensor–actor networks and enables the actor nodes to take coordinated action on a given event. Owing to various reasons such as actor mobility and low actor density, the actor network tends to get partitioned. The authors propose to use the underlying sensor nodes, which are more densely deployed, to heal these partitions. In order to maximise the utilisation of the limited energy available with the sensor nodes, a new routing protocol for actor–actor communication using directional antennas on the actor nodes is proposed. The authors contribution is threefold. First, using simulations they show that the problem of partitioning in the actor networks is significant and propose an architecture with directional antennas on actor nodes and sensor bridges to heal these partitions. Second, they identify the routing problem for this architecture based on a theoretical framework and propose centralised as well as distributed solutions to it. Third, they develop a routing protocol based on the distributed solution and show, using network simulations, that the proposed protocol not only heals the network partitions successfully, but also achieves high throughput and fairness across different flows, in addition to maximising the network lifetime.

The maximum utilization of Multi Channel - Multi Radio Wireless Mesh Networks (WMNs) can be achieved only by intelligent Channel Assignment (CA) and Link Scheduling (LS). A common CA and LS may not be optimal, in terms of utilization of underlying network resources, for every traffic demand in the network. Using the best CA and LS for every traffic demand results in channel reassignments which in turn lead to traffic disruption in the network. This makes WMNs very unreliable. In this paper, we present a simple, general, and efficient framework to quantitatively evaluate a reconfiguration policy, based on the two conflicting objectives, namely maximizing network utilization and minimizing traffic disruption. Then we propose a reconfiguration algorithm called Clustered Channel Assignment Scheme (CCAS), based on clustering of similar traffic matrices. We demonstrate the effectiveness of CCAS which mainly depends on the correlation between successive traffic matrices through extensive simulation studies.

Optical burst switching (OBS) is widely believed to be the technology for the future core network in the Internet. Traditionally, the burst header packet (BHP) is served by the control processor on first-come-first-serve basis which increases the burst loss probability (BLP) at the core nodes closer

Careful Channel Assignment (CA) and Link Scheduling (LS), tuned to the traffic demand in the network, are required to efficiently utilize Multi-Channel Multi-Radio (MC-MR) Wireless Mesh Networks (WMNs). In a dynamic network, where the traffic demand keeps changing with time, we need to reconfigure the CA and LS to suit to the changing demands. But, a change in CA leads to disruption of traffic resulting in a less reliable and lossy network. In this paper, we propose a theoretical framework to evaluate the efficiency of channel reconfiguration by taking into consideration the two conflicting objectives of maximizing network throughput and minimizing the reconfiguration overhead. A channel reconfiguration scheme that takes into account the current state of the network can find a new CA with significantly less overhead caused by reconfiguration. We propose and evaluate polynomially bounded heuristic algorithms for performing demand-based and state aware channel reconfiguration. Further, in a highly dynamic network scenario, performing reconfiguration very frequently to suit every traffic demand will lead to high reconfiguration overhead. Whereas doing it less frequently will lead to underutilization of the network. Hence, we propose a scheme that employs prediction techniques to estimate the future traffic demands in order to reduce the frequency of reconfiguration considering the long term traffic demand and conduct simulation studies to evaluate this scheme.

In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data-Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hot-spot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches.

The performance of multi-channel multi-radio (MC-MR) wireless mesh networks (WMNs) can be improved significantly with the increase in number of channels and radios. Despite the availability of multiple channels in several of the current wireless standards, only a few of them are non- overlapping and many channels are partially overlapped. In this paper, we formulate the joint channel assignment and flow allocation problem for MC-MR WMNs as a mixed integer linear program (MILP). Unlike most of the previous studies, we consider the case of using both non-overlapped and partially overlapped channels. We consider an objective of maximizing aggregate end-to-end throughput and minimizing queueing delay in the network, instead of the sum of link capacities, since the traffic characteristics of a multihop WMN are quite different from a single hop wireless network. Our formulation takes into consideration several important network parameters such as the transmission power of each node, path loss information, signal to interference plus noise ratio at a node, and frequency response of the filters used in the transmitter and receiver. We show by simulations that our MILP formulation makes efficient use of the spectrum, by providing superior channel assignments and flow allocations with the addition of partially overlapped channels, without the use of any additional spectrum.

An important issue of research in optical burst switching (OBS) networks is to minimize the loss of bursts due to contention at the intermediate nodes. These contention losses can be minimized with the design of efficient path selection algorithms at the ingress node. Path selection algorithms that learn the optimal path dynamically with the changing traffic conditions outperform the deterministic path selection algorithms. Usually in the single agent path selection algorithms, a path is selected by the agent based on the feedback received at the ingress node which does not capture the effect of the paths selected by the other nodes in the network. We develop a multi-agent approach for path selection that includes the effect of the selection made by all the other nodes in the network. The proposed path selection algorithm uses agents at different source nodes to collectively learn the network dynamics and select the best outgoing path for each burst. We present simulation results to demonstrate the effectiveness of the proposed algorithm over the other similar algorithms in the literature.

In this paper, we consider the problem of information discovery in wireless sensor networks (WSNs), where the search initiator is unaware of any of the gamma locations of target information. One of the fundamental techniques which is used for this purpose is random walk since it has several advantages like low cost (number of bytes transmitted) compared to flooding, load balancing among nodes, and minimal state maintenance. Even though random walk reduces cost, it is still high enough for energy constrained networks like WSNs. Furthermore, random walk incurs high latencies making it infeasible for delay sensitive applications. To alleviate the above mentioned problems in random walk, we propose a variant of random walk called level biased random walk (LBRW). In LBRW, the search packet traverses from the sink node (search initiator) to the circumference nodes (nodes without children) of the network and vice versa via random paths. The idea is to improve the node coverage of LBRW compared to that of random walk by forcing it to move in some particular directions. We show by extensive simulations that the cost and latency of LBRW are only 56-69% of that by random walk, when gamma=3 and at reasonable densities.

Contention losses which usually do not indicate congestion is a major issue that hinders the deployment of optical burst switching (OBS) networks. Development of efficient path and wavelength selection algorithms is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we handle path selection and wavelength selection in a joint fashion. We formulate the problem of selecting a pair of path and wavelength jointly as a multi-armed bandit problem (MABP) and discuss the difficulties in solving MABP directly. We then rewrite the Q-learning formalism to solve the MABP without explicit model in an online fashion and propose an algorithm to solve the problem near-optimally. The proposed algorithm selects a pair of path and wavelength at each ingress node to minimize the BLP on the long run. Simulation results demonstrate the effectiveness of our algorithm in minimizing the BLP with better link utilization compared to the other proposals in the literature.

In this paper, we consider the problem of information discovery in a densely deployed wireless sensor network (WSN), where the initiator of search is unaware of the location of target information. We propose two protocols: increasing ray search (IRS), an energy efficient and scalable search protocol, and k-IRS, an enhanced variant of IRS. The priority of IRS is energy efficiency and sacrifices latency whereas k-IRS is configurable in terms of energy-latency trade-off and this flexibility makes it applicable to varied application scenarios. The basic principle of these protocols is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The rays are organized such that if the search packet travels along all these rays, then the entire terrain area will be covered by its transmissions while minimizing the overlap of these transmissions. In this way, only a subset of total sensor nodes transmits the search packet to cover the entire terrain area while others listen. We believe that query resolution based on the principles of area coverage provides a new dimension for conquering the scale of WSN. We compare IRS and k-IRS with existing query resolution techniques for unknown target location such as expanding ring search (ERS), Random walk search, and variants of Gossip search. We show by analysis, simulation, and implementation in testbed that IRS and k-IRS are highly scalable, the cost of search (total number of transmitted bytes) is independent of node density, and it is much lower than that of existing proposals under high node density.

Optical burst switching (OBS) is considered as a contending technology for the core of the Internet in future. However, due to lack of the buffers, losses occur due to contention among simultaneously arriving bursts at the core nodes. Contention losses do not necessarily indicate a situation of congestion in the network. Thus differentiation (classification) of losses is essential in many applications to avoid false identification of congestion. In this paper, we propose a loss classification technique for the OBS networks based on machine learning techniques. We devise a new measure to differentiate between congestion and contention losses, which is derived from the observed losses, called the number of bursts between failures (NBBF). We observe that the NBBF follows a Gaussian distribution with different parameters for contention and congestion losses. This feature is used in differentiation. We use both a supervised learning technique (hidden Markov model (HMM)) and an unsupervised learning technique (expectation maximization (EM) clustering) on the observed losses and classify them into a set of states (clusters) after which an algorithm differentiates between the congestion and contention losses. We also demonstrate the use of loss differentiation in improving the performance of transport control protocol (TCP) over OBS networks. We modify congestion control mechanism of TCP suitably to arrive at two variants of TCP, HMM-TCP and EM-TCP. Their performance is compared with TCP NewReno, TCP SACK, and Burst TCP (X. Yu et al., Mar. 2004). Simulation results demonstrate the effectiveness and accuracy of the loss classification technique in different network scenarios.

It is widely accepted that retransmitting bursts in Optical Burst Switching networks improves the throughput at higher layers. Since the data is transported in large bursts and there are burst losses due to contention, indiscriminate retransmission can defeat the purpose of burst-level retransmissions. For many applications, retransmission after a certain time would be of no use and it may not be necessary to retransmit all the lost packets. We propose a framework for retransmission with parameters to control the retransmission rate and thus the increase in the network load. We propose a network model for controlled retransmission and a modified functional architecture of the ingress node. The existing work lacks an accurate analysis to estimate the impact of retransmissions on the network load while studying the improvement in the end-to-end packet recovery. We provide theoretical analysis to evaluate the load at each node due to both fresh and retransmitted bursts. We propose some metrics to quantify the benefit of retransmission and the impact of the retransmission parameters proposed on the network performance. We show that with controlled retransmission, the buffer requirement at the ingress node is proportional to the steady-state rate of retransmitted bursts. We validate the analytical model arid also study the impact of retransmission on the network performance with extensive simulations.

Providing real-time voice support over multihop ad hoc wireless networks is a challenging task. To make a voice application feasible over ad hoc wireless networks, the perceived voice quality must be improved while not significantly increasing the packet overhead. We suggest packet-level media-dependent adaptive forward error correction (FEC) at the application layer in tandem with multipath transport for improving the voice quality. Since adaptive FEC masks packet losses in the network, at the medium access control (MAC) layer, we avoid retransmissions in order to reduce the control overhead and end-to-end delay. Further, we exploit the combined strengths of layered coding and multiple description (MD) coding for supporting error resilient voice communication in ad hoc wireless networks. We propose an efficient packetization scheme in which the important sub-stream of the voice stream is protected adaptively with FEC depending on the loss rate present in the network and is transmitted over two maximally node-disjoint paths. Our scheme achieves significant gains in terms of reduced frame loss rate, reduced control overhead, and minimum end-to-end delay and almost doubles the perceived voice quality compared to the existing approaches.

In the deployment of wireless mesh networks (WMNs) the placement of Mesh Nodes (MNs) is an important design issue. The performance of WMNs is greatly affected by the location of the MNs. As it is difficult to place the MNs in a regular pattern in the real deployment, finding the optimal locations in the deployment environment is of much interest for the service providers. For a given possible locations for the MNs and the user density in the deployment environment, we aim to find the locations of the MNs to be used that maximizes the coverage and the connectivity of the network together. Due to high computational complexity of the exhaustive searching algorithm, an efficient local searching algorithm is proposed. Numerical results show that, the local search algorithm can give close to optimal performance with much lower time complexity than exhaustive searching.

Optical burst switching (OBS) is envisioned as the paradigm for a high-bandwidth Internet. Losses due to contention is a serious problem in OBS networks. Since the core nodes have limited capabilities, loss rate cannot be determined at the nodes. Thus, estimating the losses on a path purely based on end-to-end measurements and thereafter develop proactive measures for loss reduction is an attractive option. For the first time, we apply a tomographic technique that can estimate the losses at the core nodes from end-to-end measurement between the edge nodes. We use passive unicast tomography to minimize the network overhead. We model the problem of estimating loss rate at the nodes from path-level measurements as a maximum likelihood problem and solve it using the expectation-maximization algorithm. In simulations, we use a multiple source, multiple destination embedding on the NSFNET topology and observe losses on multiple paths to infer losses at the core nodes. The estimated losses are found to match closely with actual losses measured.

Study of the performance of transmission control protocol (TCP) over optical burst switching (OBS) networks has been an important problem of research lately. In this work, we propose an analytical model for a TCP NewReno source to derive the steady-state throughput in presence of burst assembly process and burst losses. The source model uses a Markov chain based evolution of congestion window and the network model characterizes the distribution of burst size for a general assembly process which is used to estimate the impact of a burst loss on the number of packets lost. A fixed-point iteration method is then used to jointly solve the source model and the network model to obtain the TCP send rate. We validate the proposed analytical model through simulations. Results highlight the importance of accounting for slow start and fast retransmit phases in the model.

In a wireless sensor network, the network lifetime is an important issue when the size of the network is large. In order to make the network scalable, it is divided into a number of clusters. In each cluster, a set of nodes is grouped and coordinated by a cluster-head. Due to high computations and data forwarding tasks at the cluster-heads, cluster-heads get drained out sooner. Hence, high power nodes are used as cluster- heads. Cluster-heads that are far away from the sink transmit their data packets via intermediate cluster-heads in a multi-hop fashion to the sink. Thus the cluster-heads that are near to the sink get overloaded and drained out sooner due to which the network lifetime decreases. In order to balance the lifetime of the cluster-heads, the existing works try to find either the optimum density or the optimum transmission range for the cluster-heads. The cluster-head placement algorithms should consider the fact that the node density and their transmission ranges are related parameters. In order to balance the lifetime of the cluster- head nodes and achieve the maximum network lifetime with the minimum network cost, we propose a novel optimal placement of cluster-heads (OPC) algorithm. We formulate an optimization problem and find the optimum number of cluster-heads to be deployed and their optimum transmission ranges for the given network parameters. In addition, we show that our algorithm performs better than the existing algorithm, in terms of network lifetime and network cost through the simulation results.

In this paper, we address the problem of voice multicasting in ad hoc wireless networks. The unique characteristics of voice traffic (viz. small packet size, high packet rate, and soft realtime nature) make conventional multicasting protocols perform quite poorly, hence warranting application-centric approaches in order to increase robustness to packet losses and lower the overhead due to high packet rate. By exploiting the path diversity and the error resilience properties of multiple description coding (MDC), we propose a robust voice multicast routing (RVMR) protocol. Our protocol uses a novel path based Steiner tree heuristic to reduce the number of forwarders in each tree, and constructs two trees in parallel with reduced number of common nodes among them. Moreover, unlike other on-demand multicast protocols, RVMR specifically attempts to reduce the periodic (non on-demand) control traffic. We propose several optimizations for reducing the overhead while transmitting high packet rate voice traffic in ad hoc networks. We extensively evaluate RVMR in the NS-2 simulation framework and show that it out-performs existing single-tree and two-tree multicasting protocols.

Providing real-time voice multicasting over multi-hop ad hoc wireless networks is a challenging task. The unique characteristics of voice traffic (viz. small packet size, high packet rate, and soft real-time nature) make conventional multicasting protocols perform quite poorly, hence warranting application centric approaches in order to provide robustness against packet losses and lower the overhead due to high packet rate. In this paper, we first show that the optimal voice multicasting tree (OVMT) problem is NP-complete and then propose a localized distributed heuristic for minimum number of transmissions (LDMT). By incorporating LDMT in ADMR protocol, extensive simulations are done in NS-2 framework to measure the performance of LDMT for voice applications. We observed that LDMT reduces the redundant transmissions in transmitting voice packets from the source to all multicast receivers (thus reducing the overall voice traffic considerably), thereby making it suitable for voice multicasting in AWNs.

Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Contention among the bursts that arrive simultaneously at a core node leads to burst loss which affects the throughput of higher layer traffic. Development of efficient algorithms for path selection and wavelength selection is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we formulate path selection and wavelength selection in OBS networks as a multi-armed bandit problem and discuss the difficulties to solve them optimally. We propose algorithms based on Q-learning to solve these problems near-optimally. At an egress node, the path selection algorithm evaluates the Q values for a set of precomputed paths and chooses a path that corresponds to minimum BLP. Similarly, Q-learning algorithm for wavelength selection selects a wavelength in a pre-routed path such that the BLP is minimized. We do not assume wavelength conversion and buffering at the core nodes and hence, selection of path and wavelength is done only at the edge nodes. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the other adaptive algorithms available in the literature.

In this paper, we address the problem of video multicasting in ad hoc wireless networks. The salient characteristics of video traffic make conventional multicasting protocols perform quite poorly, hence warranting application-centric approaches in order to increase robustness to packet losses and lower the overhead. By exploiting the path-diversity and the error resilience properties of multiple description coding (MDC), we propose a robust demand-driven video multicast routing (RDVMR) protocol. Our protocol uses a novel path based Steiner tree heuristic to reduce the number of forwarders in each tree, and constructs multiple trees in parallel with reduced number of common nodes among them. Moreover, unlike other on-demand multicast protocols, RDVMR specifically attempts to reduce the periodic (non on-demand) control traffic. We extensively evaluate RDVMR in the NS2 simulation framework and show that it outperforms existing single-tree and two-tree multicasting protocols.

The performance of Transmission Control Protocol (TCP) over Ad hoc wireless networks (or simply ad hoc networks) has been extensively studied through simulations by the research community. Although many theoretical models, such as [1], have been proposed for estimating the performance of TCP over wired networks, researchers have faced many difficulties in modeling TCP over ad hoc networks. These difficulties are mainly due to the behavior of the underlying physical and MAC layers. Recently, [2] attempted to solve this problem by simplifying the behavior of TCP, besides assuming that no packet losses occur. In this work, we attempt to provide a theoretical model for TCP by considering the main phases of TCP, namely the slow start phase and the congestion avoidance phase, thus providing a more accurate model that captures all of its main features. To the best of our knowledge, ours is the first model that considers the slow start phase while analyzing TCP's performance in ad hoc networks. We make use of multi-dimensional Markovian chains to model each of these phases. We then use the resulting steady state probabilities to estimate the goodput. Furthermore, the analysis is validated by comparing the theoretical and simulation results using various error models.

Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Development of efficient algorithms for path selection and wavelength selection is crucial in minimizing the burst loss probability (BLP) in OBS networks. In this paper, we present novel Reinforcement Learning algorithms for path selection and wavelength selection in the context of OBS networks. We develop an online path selection algorithm based on Q-learning to minimize the BLP by choosing an optimal path among a set of predetermined routes between every pair of ingress and egress nodes. We also propose a Q-learning algorithm for wavelength selection that selects an optimal wavelength among the available wavelengths in a pre-routed path with an objective of minimizing the BLP. We assume no wavelength conversion and buffering to be available at the core nodes of the OBS network. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the best known adaptive techniques for path selection and wavelength selection available in the literature.

In this paper, we discuss issues involved in developing autonomic Optical Burst Switched (OBS) networks. We develop an OBS network system, the first of its kind, which is self-aware, self-protecting, and self-optimizing, which are essential requirements of an autonomic network system. We use learning automata to autonomously learn the network state and make intelligent choices of route and wavelength, for burst transmission. We develop, for the first time, a self-protecting mechanism, to guard against contention losses and to adapt to network component (link/node) failures. For each connection (flow), at any point of time, this system either works without protection or chooses from one of many available protection mechanisms, based on the current network conditions and the performance requirements. Further, we develop a self-restoration mechanism based on deflection routing, wherein learning automata are used to identify an efficient alternate route to the destination, when there is a failure on the primary route. We show through extensive simulation studies that our mechanisms significantly improve burst loss probability over their existing counterparts.

The use of Wireless in Local Loop (WiLL) has generated considerable interest due to the advantages it offers such as ease and low cost of deployment and maintenance. With an increase in the number of subscribers in the network, it becomes expedient to employ spectrum reusability techniques such as the use of multihop relaying in order to improve the capacity of the wireless systems. Throughput enhanced Wireless in Local Loop (TWiLL) is one such architecture that employs multihop relaying and shortcut relaying to reuse bandwidth in WiLL Systems. Compared to other multihop wireless network architectures, TWiLL architecture assumes significance due to its potential use in fixed wireless broadband services such as LMDS (Local Multipoint Distribution Service) and MMDS (Multichannel Multipoint Distribution System). Analysis of the Call Acceptance Ratio (CAR) in multihop wireless architectures including TWiLL is nontrivial as the Erlang B formula no longer holds. In this paper, we build multidimensional Markov Chains to analyze the performance of multihop wireless systems such as TWiLL that has multiple types of channels. We also compare the results of our analysis with results from simulations. We observe that multihop relaying and shortcut relaying lead to a significant increase in the CAR of WiLL systems. Also, the free space propagation model that is normally used to model the radio channel is a very unrealistic model and does not consider reflection, diffraction, scattering, and multipath propagation that hinder transmissions in WiLL systems. In this paper, we studied the effect of several realistic radio channel propagation models on the performance of the TWiLL system through analysis and simulations.

The use of traditional TCP, in its present form, for reliable transport over ad hoc wireless networks (AWNs) leads to significant degradation in the network performance, in terms of reduction in average network throughput and increase in packet losses. This is primarily due to the congestion window updation and congestion control mechanisms employed by TCP. TCP follows a deterministic approach for updating the size of the congestion window, which is less suitable for loss-prone AWNs as it leads to very frequent occurrences of congestion in the network. Another reason is TCP invokes the congestion control mechanism for both congestion and wireless losses, as it cannot distinguish between them. Hence, in order to use TCP in AWNs, an efficient mechanism must be provided with TCP for updating the size of the congestion window based on the network conditions and distinguishing the congestion losses from wireless losses. In order to address these problems, we propose Learning TCP, a novel learning automata based reliable transport protocol for AWNs, which efficiently adjusts the size of the congestion window and thus reduces the packet losses. The key idea behind Learning-TCP is that, it dynamically adapts to the changing network conditions by observing the occurrence of events, such as arrival of acknowledgment (ACK) and duplicate ACK (DUPACK) packets and appropriately updates the congestion window size. In addition to this, we use a deterministic approach for packet loss discrimination, in order to take the appropriate action for each type of loss. Learning-TCP, unlike other existing proposals for reliable transport over AWNs, does not require any explicit feedback, such as congestion, link failure, and available bandwidth notifications, from the network. We provide extensive simulation studies of Learning-TCP under varying network conditions that show increased throughput and reduced packet loss compared to that of the traditional TCP.

Integrated services schemes have not been used to support QoS in OBS networks, largely because of the high control overhead involved in monitoring bursts continuously at intermediate nodes. In this paper, we propose for the first time, an integrated services scheme to support elastic QoS in OBS networks. Our scheme relieves intermediate nodes of the burden of monitoring individual bursts continuously, thus largely decreasing the control overhead. We show how absolute bandwidth guarantees can be provided, and how bandwidth used by each connection can be made elastic. We demonstrate the effectiveness of our scheme using simulation studies.

The issue of providing Quality of Service (QoS) guarantees in an Ad~hoc wireless network is a very challenging problem. In this paper, we make the following contributions: (i) analytically derive bounds for the end-to-end call acceptance rate using existing queueing theory methods, (ii) study the impact of the routing scheme on the end-to-end call acceptance rate, and (iii) propose a differentiated services scheme for deterministically providing QoS guarantees.Unlike existing studies which analyze the transport capacity, we focus on the end-to-end call acceptance. The framework that we assume is that of a TDMA-based Ad~hoc wireless network. The routing scheme employed influences the end-to-end call acceptance of the network. The metrics that we consider are the call acceptance probability and the system saturation probability (i.e., the probability that the network is in a state in which every new call is rejected). We derive general bounds on the call acceptance and the system saturation for the case of differentiated-classes of users in the network. These bounds indicate the number of calls of the highest priority class that can be admitted into the network.Simulation studies were carried out to study the effect of load, hopcount, and the influence of the routing protocol on the call acceptance. The increase in the call acceptance rate with the introduction of load-balancing highlights the importance of load-balancing in enhancing the system performance. From these studies, we arrive at the following results: (i) load-balancing leads to significant improvement in the end-to-end call acceptance rate, and is an important factor in attaining the maximum end-to-end call acceptance rate in a given network and (ii) it is indeed possible to provide deterministic QoS guarantees for a designated set of nodes which are characterized by "deterministic guarantee limit".

In wireless sensor networks, efficient usage of energy helps in improving the network lifetime. As the battery of a sensor node, in most cases, cannot be recharged or replaced after the deployment of the sensors, energy management becomes a critical issue in such networks. In order to detect an event, a sensor network spends majority of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low-power sleep mode. This can be achieved by using a dual frequency radio setup. However, such energy saving protocols increase the latency encountered in setting up a multihop path. We, in this paper, propose a reservation scheme, latency minimized energy efficient MAC protocol (LEEM), which is a novel hop-ahead reservation scheme in a dual frequency radio to minimize the latency in the multihop path data transmission by reserving the next hop's channel a priori. Thus, in a multihop sensor network, a packet can be forwarded to the next hop, as soon as it is received by a sensor node, which helps in eliminating the delay incurred for setting up the path. Simulation results show that LEEM consumes lesser power and reduces end-to-end latency by around 50% than that of the existing schemes

Sensor networks consist of small wireless sensor nodes deployed randomly over an area to monitor the environment or detect intrusion. The coverage provided by sensor networks is very crucial to their effectiveness. Many of the important applications of sensor networks demand autonomous mobility for the sensor nodes. Early failure of sensor nodes can lead to coverage loss that requires coverage maintenance schemes. In this paper, we propose Dynamic Coverage Maintenance (DCM) schemes that exploit the limited mobility of the sensor nodes. The main objective of coverage maintenance is to compensate the loss of coverage with minimum expenditure of energy. We propose a set of DCM schemes which can be executed on individual sensor nodes having a knowledge of only their local neighborhood topology. We propose four algorithms to decide which neighbors to migrate, and to what distance, such that the energy expended is minimized and the coverage obtained for a given number of live nodes is maximized. The decision and movement is completely autonomous in the network, and involves movement of one-hop neighbors of a dead sensor node. We also propose an extension to these algorithms, called Cascaded DCM, which extends the migrations to multiple hops. We have also compared the performance of the different algorithms in terms of the improvement in coverage, average migration distance of the nodes, and the lifetime of the network.

WDM networks adapt to the changes in traffic by reconfiguring the virtual topology. Though reconfiguration is done with the objective of utilizing resources efficiently, the resulting disruption in traffic is a cause for concern. Hence, policies are formed to decide on the time (i.e., when) to trigger reconfiguration and the new virtual topology that is most beneficial to the network. We present a simple, general and flexible framework, based on the two conflicting objectives of efficient resource utilization and minimizing traffic disruption, to evaluate reconfiguration policies. Instead of re-determining the reconfiguration policy whenever the traffic changes, we present Incremental Clustering Algorithm (ICA) to pre-plan the reconfiguration policy for a fully predictable finite sequence of traffic matrices. Since full predictability of such a sequence is not possible in practice, we learn the traffic sequences in order to probabilistically predict the future ones. From an information theoretic point of view, we quantify the predictability of traffic sequences and the number of times the reconfiguration policy is re-determined for any WDM network. To optimally predict the future traffic sequences and to incur optimal cost in the re-determination of the reconfiguration policy, we propose Universal Reconfiguration Management System (URMS). A Prediction-based Incremental Clustering Algorithm (PICA) that extends ICA is used by URMS to predict the reconfiguration policy. Within URMS, the probabilities are assigned to the traffic sequences by the prediction schemes of LZ78. We performed extensive simulations to study the effectiveness and efficiency of URMS when compared to the fully predictable and totally unpredictable models. The performance of URMS improves with learning and nearly achieves the performance of a fully predictable model.

Supporting real-time traffic in ad hoc wireless networks is considered as a challenging problem. Existing bandwidth reservation mechanisms assume a TDMA environment where achieving time synchronisation is expensive in terms of resources. Heuristics that exist for slot allocation schemes assume a CDMA over TDMA model in order to alleviate the presence of hidden terminals. Slot allocation strategies in the presence of hidden terminals assume significance in a single channel system for supporting delay sensitive traffic. In this paper, we propose three heuristics for the slot allocation process in asynchronou single channel multihop wireless networks in the presence of hidden terminals. The heuristics we propose are the early fit reservation (EFR), minimum bandwidth-based reservation (MBR) and position-based hybrid reservation (PHR). The EFR heuristic assigns bandwidth link-by-link in the forward path. The MBR heuristic allocates bandwidth to the links in the increasing order of free conn-slots. The PHR heuristic assigns bandwidth for every link proportional to its position in the path. Simulation studies show that EFR performs better in terms of delay characteristics. MBR provides better call blocking performance at the cost of high end-to-end delay. PHR provides a better delay performance compared to MBR and better call blocking performance comparedto EFR. Copyright © 2004 John Wiley & Sons, Ltd.

In this paper, we address the issue of providing multimedia traffic support in asynchronous ad hoc wireless networks. Since multimedia traffic has stringent bounds on end-to-end delay, we present a resource reservation for transmitting in such traffic. The existing asynchronous MAC protocols such as RTMAC (B.S. and C. Siva Ram Murthy, August 2002) and MACA/PR (C.R. Lin and M. Gerla, March 1999) when used for multimedia traffic provide inefficient utilization of network resources and affect call acceptance ratio and call drop ratio of multimedia traffic severely. Hence in this work, we modify the RTMAC protocol for supporting multimedia traffic so that it overcomes these limitations and improves packet delivery ratio and end-to-end delay of such traffic. The core concept of this protocol is a novel slot allocation strategy for efficient utilization of the available bandwidth for carrying multimedia traffic and best-effort traffic. Extensive simulations were performed to assess the performance of the protocol under varying network conditions. The simulations clearly indicate the gains in using such a slot allocation strategy for carrying multimedia traffic.

A major issue in the energy-constrained ad hoc wireless networks is to find ways that increase their lifetime. The communication protocols for these networks have to be designed such that they are aware of the state of the batteries. In this paper, we propose a novel battery aware medium access control (BAMAC(k)) protocol that takes benefit of the chemical properties of the batteries and provides a longer life for the mobile nodes' battery. The proposed protocol extends the battery lifetime and consumes lesser percentage nominal capacity of the battery per packet transmission compared to the IEEE 802.11 and the DWOP (distributed wireless ordering protocol), respectively. A discrete chain Markov model is used to theoretically analyze the protocol, the correctness of which is verified through a detailed simulation study. The performance results show that BAMAC(k) outperforms IEEE 802.11 and DWOP MAC protocols, in terms of power consumption, fairness, and lifetime of the nodes.

The issue of providing QoS guarantees in an ad hoc wireless network is a challenging problem. Irrespective of the nature of the routing and reservation protocol used in the QoS scheme, there is an inherent limitation on the kind of QoS guarantees that can be provided. Unlike existing studies which analyze the transport capacity, we focus on the achievable capacity. The framework that we assume is that of a TDMA-based network. In this paper, we investigate the achievable capacity and the influence of routing protocols on it. The metrics that we consider are the call acceptance probability and the system saturation probability. We derive general bounds for the case of multiple-classes of users in the network. These bounds indicate the number of calls of the highest priority class that can be admitted into the network. Simulation studies were performed to study the effect of load, hopcount, and the routing protocol on the call acceptance. The increase of the call acceptance with the introduction of load-balancing highlights the importance of load-balancing in enhancing the system performance.

The introduction of the wavelength division multiplexing (WDM) into the metro environment and the rapid growth of multicast (single source and multiple destinations) applications such as video conferencing, distance learning, and online-auction initiated the need to realise multicasting in WDM metropolitan area networks (MANs) in a cost effective way. Because of their simplicity in maintenance, operation, and control, rings became dominant topologies in metropolitan networks. But, like in any other communication network, the optical network is also prone to link (fiber) failures. Because of the large amount of traffic a fiber carries, fiber failures in a WDM system would cause severe service loss or disruption. Hence, protecting such multicast sessions against link failures is an important cause of concern. This paper assumes a single link failure model and addresses the problem of multicast protection in the scenario of WDM metropolitan ring networks. The paper discussed the SONET like self-healing schemes based on unidirectional fibers and has proposed novel approaches which are based on bidirectional fibers.

One of the challenging issues in the energy-constrained ad~hoc wireless networks is to find ways that increase their lifetime. Squeezing maximum energy from the battery of the nodes of these networks requires the communication protocols to be designed such that they are aware of the state of the batteries. Traditional MAC protocols for ad~hoc networks are designed without considering the battery state. Major contributions of this paper are: (a) a novel distributed Battery Aware Medium Access Control (BAMAC(k)) protocol that takes benefit of the chemical properties of the batteries, to provide fair scheduling and increased network and node lifetime through uniform discharge of batteries, (b) a discrete time Markov chain analysis for batteries of the nodes of ad~hoc wireless networks, and (c) a thorough comparative study of our protocol with IEEE 802.11 and DWOP (Distributed Wireless Ordering Protocol) MAC protocols. The key idea proposed in this paper is to piggy-back nodes' battery-state information with the packets sent by the nodes by means of which the nodes are scheduled to ensure a uniform battery discharge. We model the operation of the battery using a discrete time Markovian chain. Using the theoretical analysis, we calculate lifetime of the battery in terms of maximum number of packets that a node can transmit before its battery drains fully. Extensive simulations have shown that our protocol extends the battery lifetime consuming 96% and 60% less percentage nominal capacity spent per packet transmission compared to the IEEE 802.11 and the DWOP MAC protocols, respectively. In general, performance results show that BAMAC(k) outperforms IEEE 802.11 and DWOP MAC protocols, in terms of power consumption, fairness, and lifetime of the nodes. We have also analyzed the factors that influence the uniform discharge of batteries and their lifetime.

One of the challenging issues in the energy-constrained ad hoc wireless networks is to find ways that increase their lifetime. Squeezing maximum energy from the battery of the nodes of these networks requires, the communication protocols to be designed such that they are aware of the state of the batteries. Traditional ad hoc wireless MAC protocols are designed without considering the battery state. We propose a novel battery aware medium access control (BAMAC) protocol that takes benefit of the chemical properties of the batteries to provide the longest life for the mobile nodes' battery. The proposed protocol extends the battery lifetime consuming 70% and 21% less percentage nominal capacity of the battery per packet transmission compared to the IEEE 802.11 and the DWOP (distributed wireless ordering protocol) respectively. A discrete chain Markov model is used to theoretically analyze the battery behavior. We have provided a detailed simulation study on the performance and the results show that BAMAC outperforms DWOP and IE EE 802.11 MAC protocols, in terms of power consumption, fairness, and lifetime of the nodes.

The rapid emergence of Wi-Fi hotspots (access points), that are aimed at providing broadband wireless access to users in and around places of commercial interest, presents the unique problem of integrating existing cellular networks with these. We present a novel architecture that explores the interoperability issues between Wi-Fi hotspots and packet cellular networks. In the presence of multiple wireless networks with different access costs, different areas of coverage and bandwidth, the user's choice to select a particular network can significantly impact user benefits and resource usage. We identify three major user profiles - bandwidth conscious, cost conscious and glitch conscious - and study their impact on resource utilization. Contrary to intuition, simulation experiments have shown that bandwidth conscious users may get fewer bandwidth resources compared to glitch conscious users at high mobility. Similarly, it was found that bandwidth conscious users achieve high packet delivery ratios, even while incurring lower costs than cost conscious users, in certain situations. Although increasing the number of access points (APs) leads to higher packet delivery ratios, system performance degrades when the number of APs becomes very large.

The use of wireless in local loop (WiLL) has generated considerable interest due to the advantages it offers such as ease and low cost of deployment and maintenance. Directional multihop wireless in local loop (DWiLL), a new architecture proposed in this paper, employs a unique combination of directional multihop relaying in the uplink and single hop relaying in the downlink to reuse bandwidth and thus improve the throughput of WiLL systems. Analysis of the call acceptance ratio (CAR) in a DWiLL system is non-trivial as the Erlang B formula no longer holds. In This work we build multi-dimensional Markov chains to analyze the performance of the DWiLL system. We observe that directional multihop relaying leads to a significant improvement in throughput of WiLL systems.

Ad hoc wireless networks have been an increasingly important area of research in the recent past. One issue of great interest in this area is the provision of real-time support. While existing military applications use reservation-based approaches to provide real-time bandwidth guarantees, these schemes are adversely affected by node mobility. We propose an enhanced real-time support scheme that uses a novel out-of-band signaling mechanism to predict future mobility patterns and take corrective action when needed. We also propose an architecture to support differentiated service classes. This helps mobility affected nodes to take proactive measures so as to offer better real-time services to bandwidth critical applications. Through extensive simulations, we show that the use of the out-of-band signaling leads to better real-time support overall, and better response to the more critical classes as needed. We also provide a theoretical analysis for estimating the probability of disruption of a real-time call.

Interplanetary ad hoc networks (IPANs) aim to establish a communication infrastructure in deep space, connecting planets, natural and artificial satellites, and mission elements. The communication links in IPANs have low bandwidth, high error rate, and high latency. The connectivity of the network is intermittent, affected by movement of planets out of range of each other and occlusion of a planet by another. Also, the satellites and mission stations are power constrained, which makes traditional routing protocols extremely expensive in terms of time and energy consumed. However, the determinism in the orbital patterns of planets and satellites enables predictive route-building and repair. The main contributions of this paper are (i) identification of the issues involved in routing over IPANs, (ii) graph-theoretic modeling of the interplanetary backbone network, and (iii) proposal of a routing protocol, MARVIN, for interplanetary backbone networks. The proposed routing protocol, MARVIN, for movement-aware routing over interplanetary networks, makes novel use of the determinism in planetary movements. MARVIN uses the ephemeris table data for planets to precisely determine the location of planets at any instant of time, and reconfigures routes a priori, in anticipation of link-breaks. This eliminates the need for expensive topology discovery and distribution of topology information. We have simulated MARVIN over the IPAN consisting of the nine planets and the moon. It can build the routing table for any period of time given the ephemeris data tables. The graph-theoretic approach also helps in defining the stability of links of the IPAN, and can be used to choose relatively long-lasting links to reduce the number of route reconfigurations. MARVIN is studied for a variety of routing metrics such as shortest path, minimum power, and minimum end-to-end delay, and sample routing tables have been presented.

Wireless networks are inherently limited by the bandwidth constraint. MAC protocols for wireless local area networks (WLANs) play the important role of arbitrating and statistically multiplexing the transmission requests of various stations contending to access the channel. The performance of IEEE 802.11 degrades in ad hoc networks because of the presence of hidden and exposed terminals. To counter these problems, we propose a new MAC protocol called interleaved carrier sense multiple access (ICSMA) protocol for ad hoc wireless network. The performance of ICSMA was compared with single channel 802.11 MAC protocol and with 802.11 MAC protocol over two channels with half the bandwidth through extensive simulation studies. Results show that ICSMA performs better with respect to throughput, access delay, throughput fairness, and delay fairness when compared with IEEE 802.11.

Multihop cellular networks (MCNs) have been proposed as a throughput enhancement alternative for traditional cellular networks. In MCNs, as opposed to traditional cellular networks, both the base station (BS) and the mobile stations (MSs) play a significant role in forwarding data. In this paper, we propose an efficient routing protocol for single interface MCNs and compare it with an existing routing protocol. The primary motivation for exploring this single interface mechanism is to provide low cost and low power consumption mobile devices. Since pricing in packet-based data traffic for MCNs is a key issue which is not addressed so far, we proposed a set of incentive-based pricing schemes for packet based traffic that are not bound by mobility or load constraints. We also suggest reimbursement based schemes that take into account the retransmission attempts made by the intermediate nodes. We compare our routing protocol single interface MCN routing protocol (SMRP) with base driven multihop bridging protocol (BMBP) and study a set of incentive-based pricing schemes for data traffic in MCNs using extensive simulation using GloMoSim.

Existing multicast routing protocols for mobile ad hoc networks can be broadly classified into two categories, tree based protocols and mesh based protocols. Mesh based protocols have high packet delivery ratio compared to tree based protocols, but incur more control overhead. The packet delivery ratio of tree based protocols decreases with increasing mobility. This is due to the occurrence of frequent tree breaks and lack of proper tree maintenance mechanisms. These tree breaks result in frequent flooding of JoinQuery packets by the multicast group member nodes which try to get re-connected to the tree. These broadcast packets collide with the data packets and reduce the efficiency of the protocol. We propose an efficient protocol which we call as preferred link based multicast protocol (PLBM). PLBM uses a preferred link approach for forwarding JoinQuery packets. Subsets of neighbors of a node are selected using a preferred link based algorithm. These nodes, termed as preferred nodes, are only eligible for further forwarding of JoinQuery packets. We also propose a quick link break detection mechanism that locally repairs broken links. Simulation results show that our protocol performs better than other existing multicast protocols in terms of packet delivery ratio and control overhead.

A control scheme which is used to set up and tear down lightpaths, should not only be fast and efficient, must also be scalable, and should try to minimize the number of blocked connections; while satisfying the requested level of fault-tolerance. In this work we propose a distributed control scheme based on preferred link approach for establishing reliability-constrained least-cost lightpaths, by choosing the reliability of a lightpath to denote the level of fault-tolerance required by the connection request. Four heuristics are proposed and their performance is studied through extensive simulation experiments. The simulation results suggest that our heuristics provide better performance compared to other distributed protocols available, in terms of average call acceptance rate, average path cost, average routing distance, and average connection setup time; when the connection requests with different levels of fault-tolerance requirements arrive to and depart from the network randomly.

Several distributed real-time applications (e.g., medical imaging, air traffic control, video conferencing) demand hard guarantees on the message delivery latency and the recovery delay from component failures. As these demands cannot be met in traditional datagram services, special schemes have been proposed to provide timely recovery for real-time communications in multihop networks. These schemes reserve additional network resources (spare resources) a priori along a backup channel that is disjoint with the primary. Upon a failure in the primary channel, its backup is activated, making the real-time connection dependable. We propose a new backup method, called segmented backups, in which backup paths are provided for partial segments of the primary path rather than for its entire length, as is done in the existing schemes. We show that our method offers: 1) improved network resource utilization; 2) higher average call acceptance rate; 3) better quality-of-service guarantees on propagation delays and failure-recovery times; 4) increased flexibility to control the level of fault tolerance of each connection separately. We provide an algorithm for routing the segmented backups and prove its optimality with respect to spare resource reservation. We detail necessary extensions to resource reservation protocol (RSVP) to support our scheme and argue that they increase the implementation complexity of RSVP minimally. Our simulation studies on various network topologies demonstrate that spare resource aggregation methods, such as backup multiplexing, are more effective when applied to our scheme than to earlier schemes.

Ad hoc wireless networks are self-organizing, dynamic topology networks formed by a collection of mobile nodes through radio links. Minimal configuration, absence of infrastructure and quick deployment make them convenient for emergency situations apart from military applications. Multicasting plays a crucial role in the application of ad hoc networks. Since bandwidth is limited in ad hoc wireless networks, a multicast routing protocol must be efficient. It is well known that tree based multicast routing protocols are efficient. But, the main drawback of these protocols is that they are not robust enough to achieve high packet delivery ratios. We propose a tree based multicast protocol in which, to make the protocol robust as well as efficient, a node joins the multicast tree by taking into consideration not only the number of forwarding nodes but also the distance between the source and receiver. For tree maintenance, we have used a localized prediction scheme which results in a high packet delivery ratio.

Traditionally, voice communication over the local loop has been provided by wired systems. However there has been an increased interest in the use of radio access technologies in local loops. Such systems which are now popular for their ease and low cost of installation and maintenance are called wireless in local loop (WLL) systems. Subscribers' demands for greater capacity have grown over the years especially with the advent of the Internet. Unlike WLL, wired local loops have responded to these increasing demands through the use of digital technologies such as ISDN and xDSL. Multihop communication has already been studied extensively in ad hoc network environments and has begun making forays into cellular systems as well. Multi-hop communication has been proven as one of the best ways to enhance throughput in a wireless network. We study the issues involved in multi-hop communication in a wireless local loop system and propose a novel WLL architecture called Throughput enhanced Wireless in Local Loop (TWiLL). Through a realistic simulation model we show the tremendous performance improvement achieved by TWiLL over WLL. We also propose pricing schemes which could be applied in such a multi-hop environment.

Ad hoc wireless networks are self-organizing dynamic topology networks formed by a collection of mobile nodes through radio links. Minimal configuration absence of infrastructure and quick deployment make them convenient for emergency situations other than military applications. Multicasting plays a very crucial role in the application of Ad hoc networks. As the number of participants increases scalability of the multicast protocol becomes an important issue. Among the existing multicast protocols On Demand Multicast Routing Protocol (ODMRP) perfo exhibits a high packet delivery ratio even at high mobility. But ODMRP suffers from higher control overhead as the network size and the number of sources increase.In this paper we propose an efficient multicast routing protocol for Ad hoc wireless networks. This protocol reduces the control overhead by dynamically classifying the sources into Active and Passive categories. The control overhead is significantly reduced by about 30% compared to ODMRP which contributes to the scalability of the protocol. We study the effectiveness of the proposed multicast routing protocol by simulation studies and the results show that the multicast efficiency is increased by 10--15% and packet delivery ratio is also improved at high network load.

Multi-hop cellular network (MCN) is an architecture proposed by Lin and Hsu (see INFOCOM 2000. IEEE, 2000) for wireless communication & MCNs combine the benefits of having a fixed infrastructure of base stations and the flexibility of ad-hoc networks. They are capable of achieving much higher throughput than current cellular systems, which can be classified as single-hop cellular networks (SCNs). This work concentrates on MCNs and SCNs using the IEEE 802.11 standard for wireless LANs. We provide a general overview of the architecture and the issues involved in the design of MCNs, in particular the challenges to be met in the design of a routing protocol. We extend the work of Lin and Hsu to enhance the throughput of such networks further. We propose a routing protocol for use in such networks. We conduct extensive experimental studies on the performance of MCNs and SCNs under various load conditions (both TCP and UDP). Then studies clearly indicate that MCNs with the proposed routing protocol are a viable alternative for SCNs, in fact they provide much higher throughput.

We have considered the problem of providing greater throughput in packet data cellular networks. We suggest a novel cellular architecture, the multi-power architecture for cellular networks (MuPAC), based on multiple data channels used at different transmission powers. We discuss path selection mechanism in the context of multi-channel systems and MuPAC. We also offer solutions to problems faced in multi-hop networks: performance degradation at high mobility, pricing issues, partitions etc. MuPAC provides increased throughput compared to existing schemes like the current day cellular systems which can be classified as single-hop cellular network (SCN), and the multi-hop cellular network (MCN). We show using extensive simulations, that MuPAC performs well over a range of node densities, under high mobility, and under high leod conditions. MuPAC can also evolve from the existing infrastructure and offers advantages to both the service provider and the users.

The proliferation of wireless networks has led to a demand for providing bandwidth guarantees to mobile users that are comparable to the ones received by users in fixed networks. Error-prone communication links and user mobility make this a challenging task. We propose a bandwidth allocation scheme for cellular networks that copes with user mobility. Our proposed scheme constructs, for each user, hierarchical clusters (HCs) which are tree-like structures having sub-clusters of cells as nodes. The HC of any user represents the cells that he is expected to visit during the lifetime of his call. Bandwidth is reserved in all cells of the HC thereby providing an estimation of future resource availability. Such estimates are used to determine the feasibility of admitting new calls. In constructing HCs, we use the velocity of the user and available data on user movement. Our scheme is adaptive since it works with any amount of available data. Results of simulations are provided to demonstrate the performance obtained using our proposed scheme.

Due to the highly dynamic topology and absence of any fixed infrastructure, stability of selected path is of prime importance in ad hoc networks. The frequent route failure and high control overhead in routing protocols lead to degraded system performance. This paper proposes a stability based distributed adaptive routing protocol for ad hoc networks, which uses the worst case lifetime of wireless links, which is obtained by linear regression of the variation of distance between nodes as the routing metric. An efficient beaconing mechanism, load balancing, pro-active and reactive route reconfiguration mechanisms are also studied with the protocol. The protocol is supported with simulation using GloMoSim.

We consider the problem of establishing dependable connections in WDM networks with dynamic traffic demands. We call a connection with fault-tolerant requirements a dependable connection (D-connection). We consider the single-link failure model in our study and recommend the use of a proactive approach, wherein a D-connection is identified with the establishment of the primary lightpath and a backup lightpath at the time of honouring the connection request. We develop algorithms to select routes and wavelengths to establish D-connections with improved blocking performance. The algorithms use the backup multiplexing technique to efficiently utilize the wavelength channels. To further improve channel utilization, we propose a new multiplexing technique called primary-backup multiplexing. Here, a connection may not have its backup lightpath readily available throughout its existence. We develop algorithms based on this technique to route D-connections with a specified restoration guarantee. We present an efficient and computationally simple heuristic to estimate the average number of connections per link that do not have backup lightpaths readily available upon a link failure. We conduct extensive simulation experiments on different networks to study the performance of the proposed algorithms.

Wavelength-division multiplexed (WDM) optical networks using wavelength routing are considered to be a potential candidate for next-generation wide-area backbone networks. The key component in such networks is the light-path network (LPN) manager. The functions of the LPN manager include setting up the logical topology, sustaining the network by monitoring traffic and network parameters, and handling node and/or link failures. Failure handling in WDM networks is of prime importance due to the nature and volume of traffic these network carry. Failure detection is usually achieved by exchanging control messages among nodes with timeout mechanisms. Failure restoration can be done either by re-routing only the failed light paths (LPs) or by reconfiguring all the existing LPs in the network. The reconfiguration approach involves finding new LPs in the faulty network (LP design) and realizing these new LPs by selectivity removing the old LPs (LP realization) with the objective of minimizing the service disruption to the ongoing calls. Existing work on the reconfiguration approach considers only LP design and ignores LP realization. In this paper, we first propose an architecture for the LPN manager highlighting the importance of LP realization in reconfiguration-based failure restoration, then we propose performance measures and heuristic algorithms for LP realization. We evaluate the effectiveness of the LP realization algorithms through simulation studies.

The issue of providing fault-tolerance in real-time communication has been a problem of growing importance. There are two basic approaches for satisfying fault-tolerant requirements in real-time communication: (i) the forward error recovery approach, and (ii) the detect and recovery approach. The first approach is well-suited for hard real-time communication, whereas the second approach is well-suited for soft real-time communication. Neither of these basic approaches is well-suited for applications which involve both hard and soft real-time communication. In this paper, we propose an integrated scheme that combines the benefits of both the basic approaches. The proposed scheme not only caters to such mixed communication requirements, but also improves the call acceptance rate significantly due to its efficient resource allocation mechanisms such as traffic dispersion and backup multiplexing. The effectiveness of the proposed scheme has been evaluated through extensive simulation studies.

With the proliferation of multimedia group applications, the construction of multicast trees satisfying quality of service (QoS) requirements is becoming a problem of prime importance. Many of the multicast applications (such as video broadcasts and teleconferencing) require the network to support dynamic multicast sessions wherein the membership of the multicast group changes with time. We propose and evaluate an algorithm for on-line update of multicast trees to adjust to changes in group membership. The algorithm is based on a concept called quality factor (QF) that represents the usefulness of a portion of the multicast tree to the overall multicast session. When the usefulness of a particular region of the tree drops below a threshold, a rearrangement technique is used to suitably modify the tree. This algorithm aims to satisfy the delay-constraints of all current group members, at the same time minimizing the cost of the constructed tree. We compare the performance of our algorithm, by simulation, with that of an off-fine Steiner heuristic; with ARIES, a previously published algorithm for on-line update of unconstrained trees; and with the algorithm proposed by Hong, Lee and Park (see Proc. of IEEE INFOCOM, pp. 1433-40, 1998) for on-line update of delay-constrained trees. The simulation results indicate that our algorithm provides excellent cost-competitiveness that is better than that provided by the algorithm described by Hong et al., minimizes changes in the multicast tree after each update, and performs favorably even when compared with the unconstrained ARIES heuristic.

We propose a new distributed route selection approach, called parallel probing, for real-time channel establishment in a point-to-point network. The existing distributed routing algorithms fall into two major categories: preferred neighbor based or flooding based. The preferred neighbor approach offers a better call acceptance rate, whereas the flooding approach is better in terms of call setup time and routing distance. The proposed approach attempts to combine the benefits of both preferred neighbor and flooding approaches in a way to improve all the three performance metrics simultaneously. This is achieved by probing k different paths in parallel, for a channel, by employing different heuristics on each path. Also, the proposed approach uses a notion called intermediate destinations (IDs), which are subset of nodes along the least-cost path between source and destination of a call, in order to reduce the excessive resource reservations while probing for a channel by releasing unused resources between IDs and initiating parallel probes at every ID. Further, it has the flexibility of adapting to different load conditions by its nature of using different heuristics in parallel, and hence, a path found for a channel would have different segments (a segment is a path between two successive IDs), and each of these segments would very well be selected by different heuristics. The effectiveness of the proposed approach has been studied through simulation for well-known network topologies for a wide range of quality-of-service and traffic parameters. The simulation results reveal that the average call acceptance rate offered by the proposed route-selection approach is better than that of both the flooding and preferred neighbor approaches, and the average call setup time and routing distance offered by it are very close to that of the flooding approach.

With the proliferation of multimedia group applications, the construction of multicast trees satisfying quality of service (QoS) requirements is becoming a problem of prime importance. Many of the multicast applications (such as video broadcasts and teleconferencing) require the network to support dynamic multicast sessions wherein the membership of the multicast group changes with time. In this paper, we propose and evaluate an algorithm called CRCDM (controlled rearrangement for constrained dynamic multicasting) for on-line update of multicast trees to adjust to changes in group membership. The CRCDM algorithm is based on a concept called quality factor (QF) that represents the usefulness of a portion of the multicast tree to the overall multicast session. When the usefulness of a particular region of the tree drops below a threshold, a rearrangement technique is used to suitably modify the tree. Our algorithm aims to satisfy the delay constraints of all current group members, at the same time minimizing the cost of the constructed tree. We compare the performance of our algorithm, by simulation, with that of an off-line Steiner heuristic; with ARIES, a previously published algorithm for on-line update of unconstrained trees; and with the algorithm proposed by Hong, Lee and Park (see Proc. IEEE INFOCOM, p.1433-40, 1998) for on-line update of delay-constrained trees. The simulation results indicate that our algorithm provides excellent cost-competitiveness that is better than that provided by the algorithm described by Hong et al., minimizes changes in the multicast tree after each update, and performs favorably even when compared with the unconstrained ARIES heuristic.

The problem of real-time communication over a multiple access network has been well studied in the past. But, there has been no prior work addressing real-time communication over a switched local area network (SLAN), where several LAN segments are connected through a switch. In this paper we propose real-time protocols for the SLAN. The proposed protocols are based on CSMA/CD with deterministic collision resolution and belong to the dynamic planning based category, where an admission test is performed for guaranteeing message transmission. This test is based on the worst case channel access time that can elapse before a particular node can transmit its message. We also compute the worst case buffer requirement at the switch. We study the effectiveness of the proposed protocoIs through extensive simulation studies.

Many time-critical applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. In this paper, we propose an algorithm to dynamically schedule arriving real-time tasks with resource and fault-tolerant requirements on to multiprocessor systems. The tasks are assumed to be nonpreemptable and each task has two copies (versions) which are mutually excluded in space, as well as in time in the schedule, to handle permanent processor failures and to obtain better performance, respectively. Our algorithm can tolerate more than one fault at a time, and employs performance improving techniques such as 1) distance concept which decides the relative position of the two copies of a task in the task queue, 2) flexible backup overloading, which introduces a trade-off between degree of fault tolerance and performance, and 3) resource reclaiming, which reclaims resources both from deallocated backups and early completing tasks. We quantify, through simulation studies, the effectiveness of each of these techniques in improving the guarantee ratio, which is defined as the percentage of total tasks, arrived in the system, whose deadlines are met. Also, we compare through simulation studies the performance our algorithm with a best known algorithm for the problem, and show analytically the importance of distance parameter in fault-tolerant dynamic scheduling in multiprocessor real-time systems.

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters.

In this paper, we propose a new I/O overhead free Givens rotations based parallel algorithm for solving a system of linear equations. The algorithm uses a new technique called two-sided elimination and requires an N/spl times/(N+1) mesh-connected processor array to solve N linear equations in (5N-log N-4) time steps. The array is well suited for VLSI implementation as identical processors with simple and regular interconnection pattern are required. We also describe a fault-tolerant scheme based on an algorithm based fault tolerance (ABFT) approach. This scheme has small hardware and time overhead and can tolerate up to N processor failures.

In this paper, the problem of optimal distribution of measurement data to be processed in minimal time on a hypercube network of sensor driven processors is considered. An analytical model is developed for solving the problem efficiently. Unlike the previous models, this model considers: 1) explicitly the setup time which constrains exploiting all the available processors; 2) simultaneous use of links to expedite the communication; 3) partial solution combining time to encompass wider class of related problems. By deriving a lower bound on the amount of data to be received by a processor for efficient distribution, a new technique called fractal hypercube is introduced here to get the optimal solution with fewer processors, An optimal iterative method for hypercubes and a near-optimal recursive method with a refinement are presented for the same with the analysis. The effect of varying the originating processor and the choice of fractal hypercube are discussed with an effective technique called processor isomorphism. This study reveals that always the fractal hypercubes outperform the other two methods, the optimal iterative method for hypercubes and the near-optimal method.

In this paper, we present an efficient technique for mapping a backpropagation (BP) learning algorithm for multilayered neural networks onto a network of workstations (NOW's). We adopt a vertical partitioning scheme, where each layer in the neural network is divided into p disjoint partitions, and map each partition onto an independent workstation in a network of p workstations. We present a fully distributed version of the BP algorithm and also its speedup analysis. We compare the performance of our algorithm with a recent work involving the vertical partitioning approach for mapping the BP algorithm onto a distributed memory multiprocessor. Our results on SUN 3/50 NOW's show that we are able to achieve better speedups by using only two communication sets and also by avoiding some redundancy in the weights computation for one training cycle of the algorithm.

Many time critical applications require predictable performance and tasks in these applications have deadlines to be met despite the presence of faults. We propose a new dynamic non preemptive scheduling algorithm for a relatively new task model called parallelizable task model where real time tasks can be executed concurrently on multiple processors. We use this parallelism in tasks to meet their deadlines and thus obtain better processor utilization compared to nonparallelizable task scheduling algorithms. We assume that tasks are aperiodic. Further, each task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of task parallelization. To study the effectiveness of our algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm (K. Ramamritham et al., 1990). We found that the success ratio offered by our algorithm is always higher than the myopic algorithm for a wide variety of task parameters. Also, we propose a resource reclaiming algorithm to reclaim resources from parallelizable real time tasks when their actual computation times are less than their worst case computation times. Our parallelizable task scheduling together with its associated reclaiming offers the best guarantee ratio compared to the other algorithmic combinations.

Optical networks based on wavelength division multiplexing (WDM) and wavelength routing are considered to be potential candidates for the next generation of wide area networks. One of the main issues in these networks is the development of efficient routing algorithms which require a minimum number of wavelengths. We focus on the permutation routing problem in multistage WDM networks which we call 2-multinets. We present a simple, oblivious probabilistic approach which solves the permutation routing problem on 2-multinets with very high probability (in the usual theoretical sense) using O(log/sup 2/N/loglogN) wavelengths, where N is the number of nodes in the network, thereby improving the previous result due to Pankaj and Gallager (1995) that requires O(log/sup 3/ N) wavelengths. Our approach is advantageous and practical as it is simple, oblivious, and suitable for centralized as well as distributed implementations. We also note that O(logN) wavelengths will suffice with good probabilistic guarantee for the case of dynamic permutation routing where requests arrive and terminate without any relation to each other. The above results are for networks with wavelength converters and we show that the use of converters can be eliminated at the expense of a factor of log N more wavelengths. We also show how our approach can be used to solve the dynamic permutation routing problem well (in practice), using O(1) wavelengths on the hypercube and O(logN) wavelengths on the Debruijn network. These improve the previous known bounds of O(logN) and O(log/sup 2/N), respectively.

We consider the problem of finding an optimal and suboptimal task allocation (i.e., to which processor should each module of a task or program be assigned) in distributed computing systems with the goal of maximizing the system reliability (i.e., the probability that the system can run the entire task successfully). The problem of finding an optimal task allocation is known to be NP-hard in the strong sense. We present an algorithm for this problem, which uses the idea of branch and bound with underestimates for reducing the computations in finding an optimal task allocation. The algorithm reorders the list of modules to allow a subset of modules that do not communicate with one another to be assigned last, for further reduction in the computations of optimal task allocation for maximizing reliability. We also present a heuristic algorithm which obtains suboptimal task allocations in a reasonable amount of computational time. We study the performance of the algorithms over a wide range of parameters such as the number of modules, the number of processors, the ratio of average execution cost to average communication cost, and the connectivity of modules. We demonstrate the effectiveness of our algorithms by comparing them with recent competing task allocation algorithms for maximizing reliability available in the literature.

Many time-critical applications require predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. Failures can happen either due to processor faults or due to task errors. To tolerate both processor and task failures, the copies of every task have to be mutually excluded in space and also in time in the schedule. We assume, each task has two versions, namely, primary copy and backup copy. We believe that the position of the backup copy in the task queue with respect to the position of the primary copy (distance) is a crucial parameter which affects the performance of any fault-tolerant dynamic scheduling algorithm. To study the effect of distance parameter, we make fault-tolerant extensions to the well-known myopic scheduling algorithm which is a dynamic scheduling algorithm capable of handling resource constraints among tasks. We have conducted an extensive simulation to study the effect of distance parameter on the schedulability of fault tolerant myopic scheduling algorithm.

In this paper, we consider the problem of solving a sparse system of linear equations using a new LU factorization based algorithm. The algorithm employs a new technique called two-sided factorization to produce the complete solution by solving only one triangular system after the factorization phase against the solution of two triangular systems in the existing LU factorization based methods. The effectiveness of the new algorithm in solving sparse linear systems on hypercubes is demonstrated using IEEE standard power system networks as benchmarks.

The multiprocessor scheduling problem is the problem of scheduling the tasks of a precedence constrained task graph (representing a parallel program) onto the processors of a multiprocessor in a way that minimizes the completion time. Since this problem is known to be NP-hard in the strong sense in all but a few very restricted eases, heuristic algorithms are being developed which obtain near optimal schedules in a reasonable amount of computation time. We present an efficient heuristic algorithm for scheduling precedence constrained task graphs with nonnegligible intertask communication onto multiprocessors taking contention in the communication channels into consideration. Our algorithm for obtaining satisfactory suboptimal schedules is based on the classical list scheduling strategy. It simultaneously exploits the schedule-holes generated in the processors and in the communication channels during the scheduling process in order to produce better schedules. We demonstrate the effectiveness of our algorithm by comparing with two competing heuristic algorithms available in the literature.>