Unmanned Aerial Vehicle (UAV) has emerged as a promising technology to provide wireless signals from air to the ground users in specific scenarios such as earthquakes, tsunamis and other disasters. The performance of the UAV is degraded when the signals are blocked by obstacles in dense urban scenarios. To address this issue and enhance the signal quality available to the ground users, Reconfigurable Intelligent Surface (RIS) has emerged as a new technological paradigm. It offers an intelligent configuration for the signal propagation environment by redirecting the signals to the users. In this article, we solve a non-convex optimization problem of RIS-assisted UAV network by jointly optimizing the RIS phase shift and 3D trajectory of UAV to maximize the energy efficiency of a rotatory-wing UAV. The considered optimization problem is solved using Deep Reinforcement Learning (DRL) based techniques in an on-line fashion to reduce the computational complexity. We leverage Twin-delayed Deep Deterministic Policy Gradient (TD3) to solve the problem by considering the UAV trajectory as a set of continuous actions. For comparison, we also use the Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG) and Double Deep Q-Network (DDQN) for continuous and discrete optimization of the UAV trajectory, respectively. Extensive simulations show that the TD3 outperforms all the considered DRL techniques with the highest energy efficiency and throughput, and the lowest propulsion energy.

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global${K}$-means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.

This article considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet-of-Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the 3-D UAV placements, transmit power, and cloud resources. This represents a challenging, nonconvex, and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems, namely, 2-D UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global$K$-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.

With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.

Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits.

This article addresses a real-time monitoring mechanism of vital signs of patients in a three-tier Internet of Medical Things-based software-defined-wireless body area network. The challenging issues are energy efficiency, interference, delay, emergency conditions, and reliability. We propose a two-tier scheduling algorithm in which Walsh Hadamard codes are employed to avoid the interference and decrease the delay and energy consumption in tier I. To schedule the transmission of different patients in tier II [i.e., the transmissions between hubs and access points (APs)], we propose a fair nonorthogonal multiple access-based scheduling algorithm, which jointly considers the channel state, energy consumption, and delay. Some processing tasks of the proposed algorithm are executed by local edge servers connected to APs and managed by a central SD-controller in tier III. Consequently, the transmission delay and energy consumption considerably decrease and the effective throughput increases. The algorithm takes the precedence of some information over other sensed data into account by employing the emergency index. The simulations results illustrate the advantages of the proposed algorithm in terms of energy consumption, network delay, and effective throughput, and the superior performance over other benchmark schemes.

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency tradeoff of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

Automation of modern industrial plants require real-time tracking of object locations and sensing of local and ambient parameters for variety of applications such as counting and tracking of objects in assembly line, detection and positioning of failures of machines etc. Mostly, discrete Real Time Location System (RTLS) performs object tracking in existing industrial automation without its integration with the sensing and control network, which constrains application's responsiveness. In this paper, we propose an integrated solution of Wireless Positioning Sensor Network (WPSN) that is designed for accuracy, reliability, scalability and optimal network operation. The proposed WPSN is applicable for harsh indoor industrial environments for not only monitoring and control of plant operations but also for identification, localization and tracking of assets and inventory in industrial warehouses. The indoor industrial environment poses challenging conditions for radio signal propagation that adversely affects reliability of communication of sensing and location data. We approach reliability by incorporating redundancy and making our network reconfigurable through adaptive intelligent learning process. We employ adaptive clustering technique to address the need of scalable deployment for varied industrial scenarios. We include hybrid localization scheme to provide high precision positioning but with fallback reduced precision operation to deal with long-term channel impairment. The WPSN is connected with backend cloud infrastructure for low cost monitoring and control.

Emergency situations in a mining environment have the potential for the loss of human life. Tracking mobile assets and personnel can significantly improve the safety of all miners. Existing distributive localization algorithms are not well suited for a mobile asynchronous mine environment. We propose the Mobile Cooperative Localization Algorithm (MCLA) to increase the accuracy and number of nodes which are localized. The proposed algorithm has been validated through simulation using ns-3 and demonstrates an increase in accuracy for a mobile distributed environment.

Directional antennas are known for spatial reuse, extended range, less interference, and less energy consumption as compared to omnidirectional antennas. In this paper, we propose an energy-efficient scheduled directional MAC (DTRAMA) algorithm for sensor networks, which is based on TRAMA with some modifications to exploit spatial reuse of directional antennas. In TRAMA, nodes achieve energy efficiency by following traffic-adaptive communication and sleep schedules. DTRAMA introduces spatial reuse checks in TRAMA to allow an otherwise sleep node to schedule transmission. Our simulation results show that DTRAMA is effective in exploiting spatial reuse of directional antennas by improving packet delivery ratio and packet delay of TRAMA. It, however, compromises energy efficiency by reducing average sleep time of a node.

In this paper, we propose an optimal power allocation scheme that minimizes the energy per bit of worst user (i.e., the user with highest energy per bit) in a multiuser wireless communication network. The problem of determining energy efficient power allocation to improve the worst user is a constrained non-convex nonlinear fractional programming problem. We propose an iterative energy efficient power allocation algorithm that guarantees optimal solution. We use parametric equivalent formulation to get the optimal solution. Numerical solutions obtained using simulations are presented and compared with equal power allocation scheme.

In this paper, we apply the cross entropy optimization (CEO) to the problem of joint multiple relay assignment and source/relay power allocation (JMRAPA) in green cooperative cognitive radio (GCCR) networks. We use shared-band amplify and forward relaying for cooperative communication in the JMRAPA problem. The proposed JMRAPA maximizes the total rate and minimizes the greenhouse gas emissions in GCCR networks. It is a non-convex combinatorial optimization problem and is NP-hard. We propose to use concave upper bound that makes it a convex problem. The effectiveness of the proposed CEO-based method is shown through simulation results.

We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy‐efficiency is a constraint non‐linear non‐convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non‐linear non‐convex fractional power allocation problem into a concave fractional programme by using Charnes–Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees ε ‐optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.

In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive radio with objective of maximizing energy efficiency is a constraint nonlinear nonconvex fractional programming (CNNFP) problem. We present the optimal power allocation in DF relaying by transforming the CNNFP power allocation problem into a concave fractional program by using Charnes-Cooper transformation. We also present an iterative ε-optimal solution for the CNNFP problem using Dinkelbach algorithm. The convergence of the iterative algorithm is proved and numerical solutions obtained using simulations for DF cooperative communications are presented.

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy‐efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non‐linear non‐convex optimisation problem. The authors first transform the energy‐efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε ‐optimal solution. A proof of convergence is also given for the ε ‐optimal algorithm. The proposed ε ‐optimal algorithm provide a practical solution for power allocation in energy‐efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.

In Multicast, since almost simultaneous communication take place along several branches of a Multicast distribution tree, these branches tend to mutually block each other due to interference. We utilized a model of scheduling in automobile production line called GORBY to divide the nodes in the Multicast distribution tree into three groups (GORby-Group or GG). We calculate the SINR of the node based on interferences caused by all the nodes within that GG, assuming when the given node is in the transmit state then all the nodes within its GG will also be in the transmit state. Finally we use the computed SINRs in our join Multicast group algorithm to achieve the most SINR, which we call it GORby Interference-Aware (GIA) metric in case of a node, and Path GIA (PGIA) in case of a path.

Cooperative relaying techniques have been shown to provide spatial diversity in fading wireless environment. As a result, they increase link reliability, provide higher capacity, reduce transmit power, and extend transmission range as opposed to non-cooperative transmission (direct transmission). Although the use of cooperative relaying has been proven to achieve those gains in the absence of interference (single flow), it is not clear how much gain we can expect from using cooperative relaying in multi-flow scenario specifically the total network capacity. The network capacity in multi-hop multi-flow settings is severely affected by interference between links and, this effect increases when the cooperative relaying is imposed. In this paper, we use a clique based capacity analysis to investigate the performance gain (loss) on network capacity for wireless ad hoc networks by using cooperative relaying. It is observed that the throughput drops significantly when cooperative links are imposed in the network.

The recent advances in antenna technology have made smart beamforming antennas attractive candidates to be deployed in multi-hop wireless networks. Beamforming antennas can increase the spatial reuse of the channel and consequently the network capacity. Due to practical and economical considerations, deploying beamforming antennas in the whole network is sometimes infeasible. In this paper, we focus on multi-hop wireless networks with heterogeneous antenna capabilities. We study the problem of optimal antenna placement to maximize the total network throughput. We first introduce an antenna-aware conflict graph using the notion of virtual links. With the objective of minimizing the size of the maximum clique, we formulate our problem as a mixed-integer linear programming problem. Moreover, we propose a beamforming antenna placement heuristic algorithm for the partial deployment of beamforming antennas to upgrade existing wireless networks originally equipped with traditional omni-directional antennas. Our numerical results show that the proposed algorithm produces near-optimal results in a small fraction of time.

Beamforming antennas promise a significant increase in the spatial reuse of the wireless medium when deployed in multihop wireless networks. However, existing directional Medium Access Control (MAC) protocols with the default binary exponential backoff mechanism are not capable of fully exploiting the offered potential. In this paper, we discuss various issues involved in the design of MAC protocols specific for beamforming antennas. Based on our discussion, we argue that the traditional binary exponential backoff mechanism limits the possible spatial reuse and aggravates some beamforming-related problems such as deafness and head-of-line blocking. To grasp the transmission opportunities offered by beamforming antennas, we design an Opportunistic Directional MAC (OPDMAC) protocol for multihop wireless networks. The OPDMAC protocol employs a novel backoff mechanism in which the node is not forced to undergo idle backoff after a transmission failure but can rather take the opportunity of transmitting other outstanding packets in other directions. This mechanism minimizes the idle waiting time and increases the channel utilization significantly and thereby enables OPDMAC to enhance the spatial reusability of the wireless medium and reduce the impact of the deafness problem without additional overhead. Through extensive simulations, we demonstrate that OPDMAC enhances the performance in terms of throughput, delay, packet delivery ratio, and fairness. To further improve its performance, we discuss and evaluate the benefits of carefully choosing some protocol parameters instead of using the default values commonly used by other directional MAC protocols.

Utilizing smart beamforming antennas in contention-based wireless mesh networks has proven to provide a significant increase in the network capacity. Nevertheless, their effectiveness in providing additional QoS guarantees is still under-explored. In this paper, we develop a framework for analyzing conflicts on wireless links considering both the physical interference and the operation of the underlying directional MAC protocol. Based on a novel taxonomy, we classify the link conflicts into several categories and propose a novel colored conflict graph abstraction to model the network interference pattern in the presence of contention-based directional MAC protocols. Using the proposed colored conflict graph, we derive a closed form expression for the probability of successful transmission over a wireless link given that all the bandwidth requirements are satisfied. Based on our analysis, we formulate the bandwidth-guaranteed routing problem as an optimization problem. Since the problem is NP-hard, we present a heuristic algorithm for joint routing and admission control to find single-path bandwidth-guaranteed routes. Using extensive simulations, we demonstrate the accuracy of our conflict analysis and the ability of the proposed algorithm to provide QoS guarantees along with efficient channel utilization.

Recently, the use of directional antennas in multi-hop wireless networks has received increasing attention in the research community due to their potential benefits and numerous advantages over omni-directional antennas. However, the majority of existing work has focused on homogeneous wireless networks where all the nodes are equipped with the same beamforming antennas. In this work, we focus on multi-hop wireless networks with heterogeneous antennas capabilities, a scenario that is expected to prevail in next generation wireless networks. We address the fundamental question: Given a specific network topology with heterogeneous antennas, what are the theoretical capacity bounds of such network? For that purpose, we formulate the problem as an optimization problem and compute the upper and lower bounds. Our results show that the capacity of heterogeneous multi-hop wireless networks can be significantly increased even with small fraction of directional nodes.

Cooperative diversity techniques have recently received a lot of attention due to their ability to provide spatial diversity in fading wireless environment, thus increases link reliability, provides higher capacity and reduces transmit power for the same level of performance. In this paper we study a joint problem of relay selection, power allocation and routing in multi-hop wireless ad hoc networks based on cooperative transmission. In particular, an optimal routing strategy is proposed to minimize the end-to-end total transmission power subject to end-to-end target rate. An ad-hoc routing strategy is proposed to find an optimal route (in terms of total transmit power minimizing) for decode-and forward strategy based on the well known Dijikstra algorithm which can be easily implemented in distributed way. Simulation results show that the proposed strategy has great improvement in terms of power saving compared with traditional non-cooperative shortest path algorithms, by more than 70% in some simulation scenario.

Although directional antennas can increase the spatial reuse in wireless ad hoc networks, the directional carrier sense multiple access/collision avoidance (CSMA/CA) protocols encounter unprecedented challenges that can offset this potential benefit. One critical problem is known as deafness that occurs when a transmitter repeatedly fails to communicate with its intended receiver because the receiver is beamformed towards another direction. The deafness problem has not yet been analytically studied since existing analytical models for directional CSMA/CA ignore the effect of deafness. In this study, the authors develop an analytical framework for directional CSMA/CA, which is the first analytical model to consider the problem of deafness as a source of transmission failures in multi-hop wireless networks with directional antennas. They also propose a deafness index to quantify the negative impact of deafness. Using their framework, the authors study the tradeoff between spatial reuse and deafness when a directional CSMA/CA protocol is employed. Their results demonstrate that decreasing the antenna beamwidth increases the saturation throughput up to a certain limit corresponding to an optimum beamwidth. However, by further lowering the beamwidth, the negative impact of deafness offsets the benefits of spatial reuse and results in a steep decrease in the saturation throughput. These results prove analytically that deafness is a critical problem if left unaddressed.

The demand for QoS has made its support an essential component in today's wireless networks. The use of directional antennas in multi-hop wireless networks is known to provide substantial increase in the utilization of the wireless medium. However its effectiveness in providing QoS assured routing is unexplored. In this paper, we study the problem of bandwidth guaranteed routing in contention-based wireless mesh networks with directional antennas. We present a detailed analysis for the interference pattern of such networks taking into account the challenges imposed by the use of directional antennas such as deafness. Based upon this analysis, we formulate the QoS routing problem as an optimization problem. We then present a routing and admission control heuristic to solve the problem. Our simulation results demonstrate the effectiveness of our routing scheme and the necessity of admission control for QoS support.

Directional antennas in multihop wireless networks offer a potential increase in the spatial reuse of the wireless channel. However, existing directional MAC protocols are not capable of fully exploiting this essential benefit. In this paper, we propose OPDMAC which is an opportunistic directional MAC protocol that is motivated by the possibility of achieving higher spatial reuse. The proposed protocol is novel in its backoff mechanism with directional antennas. If a node is forced to backoff due to a missing CTS or acknowledgement in one direction, OPDMAC explores transmission opportunity in other directions. This minimizes the idle wait time of the wireless channel and increase the channel utilization. In addition, OPDMAC is found to decrease the effect of deafness without additional control overhead. Our simulation results show that OPDMAC outperforms existing directional MAC protocols in terms of throughput, delay and fairness.

Emergency situations in mines result in loss of precious human lives. In this paper we demonstrate the viability of the architecture of a Wireless Sensor Network (WSN) in underground mines to achieve mine safety. We propose Minimum Delay Maximum Lifetime (MDML) routing in the wireless sensor network to achieve reliable low-delay routing of emergency data traffic and increased lifetime of the network by employing energy efficient routing for periodic non-emergency regular traffic. Routing metrics proposed in MDML is specifically designed considering signal propagation characteristics in underground mines. We also suggest configuring priority queue in a sensor node giving higher priority to emergency traffic. Our simulation shows that zero loss and low delay can be achieved for emergency traffic. It also shows that MDML is effective in trading slight loss of network lifetime for achieving high reliability and low-delay.

A novel approach to event boundary detection is proposed, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of the algorithm is to localize node transmission along the event boundary, since the sensory data from nodes not along the boundary is not required for event boundary detection. Simulations demonstrate that the algorithm has a linear efficiency function when related to event radius of sufficient size and further demonstrates that the boundary of an event may be successfully mapped using agents.

The access link contention can severely constrain the end- to-end throughput of the path between a source and destination mobile node connected through the backbone of the wireless mesh network (WMN). In this paper, we propose an integrated routing system for WMN that includes both the backbone paths and the ad-hoc paths formed as a result of direct communication among mobile nodes without going through the backbone. In our proposed routing system, an alternative ad-hoc path can be used only when the primary backbone path is severely constrained due to access links contention. We propose a scheme that allows the source mobile node to evaluate the throughput of the backbone and ad-hoc paths, and select one path for communicating with the destination. We implemented the proposed routing system in OPNET simulator, and evaluated the performance of our scheme under variety of conditions. Simulation results show that the alternative ad-hoc path is effective in delivering higher throughput when backbone path is severely constrained.

Maintaining the quality of service level of local and handover flows during the handover of mobile node from one cell to another in a DiffServ-capable mobile wireless access network is a challenging problem. In this paper we proposed AF2 class to be used as a transient class for handover flows. When a handover flow is expected to cause significant degradation to the bandwidth of local AF1 flows, then it is assigned to AF2 class. The AF2 flows are expected to receive better than best effort service. We developed an admission control algorithm for handover flows that determines their assignment to either AF1 or AF2. We assume Mobile-IP type handover scheme where home agent serving as a mobility anchor point can remark the flow to AF2 DSCP after the handover. We evaluated the workability of our QoS scheme and the admission control algorithm by simulating different handover situations. Our simulation shows that our algorithm does achieve its object. It also shows that the penalty control parameter β is indeed capable of protecting local flows under variety of handover situations.

is identified as a key driver of next-generation network technology under communication paradigms for 2020 initiative. The future's pervasive computing environments need all their components to become autonomous by managing their own evolution and configuration changes without explicit user or administrator action. Since there is no current routing protocol which always performs well for all kinds of possible environments and requirements, new innovative routing paradigms must be designed to best fit the next generation networks. In this paper, we present our vision of routing in Autonomic communication. More specifically, we discuss the main design issues for the new routing paradigms. Moreover, some research directions and challenges are also addressed.

Content Delivery over Internet is optimized for low user perceived latency by designing specialized Content Delivery Networks (CDN). Request Routing systems in CDNs generally achieve low user latency by balancing the server load and selecting the nearest server. Although Traffic engineering is another factor that impacts the client latency, which is not generally integrated with the Request Routing. In this paper we proposed a CDN architecture that employs a TE-Friendly Request Routing (TFRR) system, which not only distributes the server load but also achieves network efficiency by balancing the path loads. We also presented a detailed TFRR system design and an algorithm to perform the TFRR function. Our simulation results show that the proposed CDN with our TFRR algorithm achieves both server and traffic load balancing.

RAON is a P2P system for MANET that performs query-forwarding decision taking into account link instability and power constraints. File download traffic causes significant drop in its query forwarding performance. We propose two modifications to RAON, called CAON, to reduce the effect of congestion on the query forwarding performance caused by the download traffic: (1) local feedback mechanism; and (2) download site selection algorithm to avoid overlapping download and unstable query traffic paths. Our simulation results show that CAON improves the success rate and delay of query search on RAON with low overhead.

Although cellular networks such as GPRS (2.5G) or UMTS (3G) have achieved both circuit switching and packet switching services with wide area coverage, they still fall short of meeting the high data rate demands of mobile users. Integrating cellular network with other high-speed and low-cost wireless technologies is a key challenge for migrating to 4G. The IEEE 802.11 technology that has been well-developed and widely used in local area networks seems to be a proper choice for fulfilling users' expectations in hotspots. Its ad hoc mode of operation allows mobiles users to achieve connectivity with low infrastructure support. This paper proposes a design to achieve the internetworking between 802.11 mobile ad hoc network (MANET) and UMTS. The key approach in our design is to model inter-system handover as inter-SGSN handover, and connect gateway in the ad hoc network with the GGSN

Handover performance is very important when evaluating IP mobility protocols. If not performed efficiently, handover delays, jitters, and packet loss directly impact application performance. We propose a new architecture for providing efficient handover, while being able to coexist with other protocols. We propose a paradigm for multicast-based micromobility (M&M), where a visiting mobile is assigned a multicast address to use while moving within a domain. The multicast address is obtained using algorithmic mapping, and handover is achieved using multicast join/prune mechanisms. This paper outlines a framework for the design and evaluation of micromobility protocols. We define a suite of protocols (called candidate access router set) to enable multiple-access routers to receive traffic for the mobile node. By changing the number of such routers, timing, and buffering parameters, the protocol may be fine-tuned for specific technologies (e.g., 802.11) and handover scenarios. Extensive NS-2 simulations are used to compare M&M with other micromobility schemes-cellular Internet protocol (CIP) and handoff-aware wireless access Internet infrastructure (HAWAII). For proactive handover scenarios, our results show that M&M and CIP show lower handover delay and packet reordering than HAWAII. M&M, however, handles multiple border routers in a domain, where CIP fails. Also, for scenarios of reactive handover and coverage gaps M&M clearly outperforms CIP and HAWAII.

Cellular networks, e.g. UMTS provide voice and data services to mobile users. In hot spots where users need high speed data services operators can deploy low-cost high-speed WLANs, e.g. 802.11, to cover hot-spots. This paper proposes a possible architecture of integrating UMTS and 802.11 WLAN. The architecture allows a mobile node to maintain data (PS) connection through WLAN and voice (CS) connection through UMTS in parallel. This is especially attractive because WLAN is currently used primarily for high-speed best-effort data service only.

Abstract There is a growing interest in providing Internet services to the mobile nodes. When mobile nodes travel from one router's service area to another, the time‐sensitive applications may see degradation in service. We investigate into the effects of handoff on service quality of mobile nodes. Several experiments are conducted using various packet metering and marking schemes with or without transferring profiles to the new router. Results indicate the relative instability period following handoff, loss of packets and delay encountered by packets in profiled or un‐profiled handoffs, leading to determining suitable mix of metering and marking schemes with or without context transfer. Copyright © 2002 John Wiley & Sons, Ltd.

The authors present a storage model for temporal databases that accommodates large amounts of temporal data. The model supports efficient search for object versions based on temporal conditions, using a time index. They define an access structure, the monotonic B/sup +/-tree, that is suitable for implementing a time index for append-only temporal databases. The storage model uses a combination of magnetic disks and write-once optical disks to keep current, past, and even future states of a database online and readily accessible. It provides an automatic archiving of both object versions and time index blocks to optical disks.>

An approach to the inference problem in database security is described. This new approach is based on existing ideas in query containment theory. This theory may be used to generalize query modification, a query answering approach that allows a user to write a query that is beyond his access privileges, but the system will construct a set of similar queries that is within his privileges. This generalization may also be used to approach the inference problem. Examples of formalizing inference problems within the framework of queries are given, along with an algorithm for the detection of inference violations. Finally, suggestions are made for query answering to avoid inferences at several granularities by blocking all answers for a query, component queries, individual tuples, or individual values.>