Network-assisted resource management for wireless data networks

We propose a framework for network-assisted radio resource management in wireless data networks. This type of radio resource management techniques offer implementation and capacity benefits compared to conventional, interference-measurement based, dynamic channel assignment (DCA) algorithms. The basic idea is to use interbase signaling to shift most of the burden of the resource allocation from the air interface to the backbone infrastructure. By exchanging channel assignment as well as other relevant information in real time through the backbone network, each base can calculate the impact of a resource assignment on the system. As a result, rapid interference measurements, which are typically needed to implement DCA schemes, are replaced by a limited amount of path loss measurements and the computation of interference conditions by the base stations. This significantly reduces the measurement and over-the-air signaling requirements, and can also provide an opportunity for a better optimization of the system performance. We focus on two specific algorithms: network-assisted least-interference-based dynamic packet assignment (NA-LI-DPA) and network-assisted dynamic packet assignment with throughput optimization (NA-DPA). NA-LI-DPA closely resembles a least-interference-based dynamic channel assignment algorithm, and NA-DPA attempts to further improve the overall system throughput. The algorithms, as defined, are appropriate for a best-effort data service, where the primary goal is to provide a higher throughput. However, it will be clear from the discussion that it is also feasible to alter the algorithms to optimize performance metrics other than throughput, e.g., to ensure a certain quality of service. We show through simulation that, for a system like enhanced general packet radio service (EGPRS) system, NA-DPA can provide a throughput that is 50% higher than random packet assignment, and 25% higher than that obtained by conventional DCA algorithms     

An AHP-based resource management scheme for CRRM in heterogeneous wireless networks

In a heterogeneous wireless environment, a variety of Radio Access Technologies (RATs) coexist. Since the number of RATs is anticipated to increase in the near future, it is desirable to have radio and network resources managed in a cooperative manner using the Common Radio Resource Management (CRRM) strategy. In order to make RAT-specific radio resources manageable in CRRM, this paper proposes the Analytical Hierarchy Process (AHP) based resource management scheme that efficiently allocates resources among heterogeneous wireless networks. The proposed AHP-based method is simple and flexible enough to be used in any network environment and can consider a multitude of decision factors. In addition, the proposed scheme uses a radio bandwidth model, which properly reflects transmission rates under given channel conditions, as the actual radio resources to be allocated. The model considers the AMC (Adaptive Modulation and Coding) scheme that is widely used in current broadband wireless access technologies, and thus, packet service characteristics, such as response time, can be analyzed. This is in contrast to existing work that focuses only on circuit service characteristics (e.g., blocking probability). The effectiveness and flexibility of the proposed method are demonstrated by implementing a number of existing methods and performing extensive simulation study on several different scenarios.     

An efficient data dissemination model for wireless sensor networks

Wireless Networks - In a wireless sensor network, one of the most important constraints on sensor nodes is their power source, which is a battery. Sensor nodes carry a limited and generally...     

An efficient and fair cooperative approach for resource management in wireless networks

In cellular networks, the implementation of various resource management processes, such as bandwidth reservation and location updates, has been based on the one‐to‐one resource management information exchange paradigm, between the mobile nodes and the base stations. In this paper, we design and demonstrate the use of a distributed cooperative scheme that can be applied in the future wireless networks to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer‐to‐peer communication concept of wireless ad hoc networks. In our approach, the network is subdivided into one‐hop ad hoc clusters where the members of each cluster cooperate to perform the required management functions, and conventional individual direct report transmissions of the mobile terminals to the base stations are replaced by two‐hop transmissions. The performance evaluation and the corresponding numerical results presented in this paper confirm that our proposed scheme reduces significantly the overall system energy consumption when compared with the conventional one‐to‐one direct information management exchange approach. Furthermore the issue of fairness in dynamically selecting the various cluster heads in successive operational cycles of the proposed scheme is analyzed, and an enhanced algorithm is proposed and evaluated, which improves significantly the cluster head selection fairness, in order to balance the energy consumption among the various mobile terminals. Copyright © 2005 John Wiley & Sons, Ltd.     

An enhanced radio resource management based MIH policies in heterogeneous wireless networks

The require of omnipresent wireless access and high data rate services are expected to increase extensively in the near future. In this context, heterogeneous networks, which are a mixture of different wireless technologies (LTE-advanced, LTE-advanced Pro, C-IoT (Cellular Internet of Thing), 5G WiFi, etc) are invited to enable important capabilities, such as high data rates, low latencies and efficient resource utilization in order to provide dedicated capacity to offices, homes, and urban hotspots. Mixing these technologies in the same system, with their complementary characteristics, to afford a complete coverage to users can cause various challenges such as seamless handover, resource management and call admission control. This article proposes a general radio resource management framework which can be supported by future network architectures. A combined call admission control, resource reservation algorithm and bandwidth adaptation based IEEE 802.21 MIH standard approach for heterogeneous wireless network is detailed in this framework. Our aims are to guarantee quality of service (QoS) requirements of all accepted calls, reduce new call blocking probability and handover call dropping probability, and maintain efficient resource utilization. Performance analysis shows that our proposed approach best guarantees QoS requirements.     

A comprehensive resource management framework for next generation wireless networks

We propose an integrated resource management approach that can be implemented in next generation wireless networks that support multimedia services (data, voice, video, etc.). Specifically, we combine the use of position-assisted and mobility predictive advanced bandwidth reservation with a call admission control and bandwidth reconfiguration strategy to support flexible QoS management. We also introduce a mobile agent based framework that can be used to carry out the functions of geolocation and of the proposed resource management in wireless networks. A model is also developed to obtain the optimal location information update interval in order to minimize the total cost of the system operation. The comparison of the achievable performance results of our proposed scheme with the corresponding results of a conventional system that supports advanced bandwidth reservation only, as means of supporting the QoS requirements, demonstrate that our integrated scheme can alleviate the problem of overreservation, support seamless operation throughout the wireless network, and increase significantly the system capacity.     

Radio resource management in future wireless networks: requirementsand limitations

Comparing market estimates for wireless personal communication and considering proposals for wideband multimedia services with the existing spectrum allocations for these types of systems show that spectrum resource management remains an important topic in the near and distant future. In this article the authors start by presenting a quite general formulation of the radio resource management problem where the three key allocation decisions are concerned with waveforms (“channels”), access ports (or base stations), and, finally, with transmitter power. Some approaches to these problems found in the literature are reviewed. In particular, the principles of random channel allocation schemes, as found in frequency-hopping or direct-sequence CDMA systems, are compared with deterministic dynamic channel allocation schemes. The article closes by giving an outlook of some of the key problems in resource management in future wireless multimedia systems     

Distributed resource management in wireless sensor networks using reinforcement learning

In wireless sensor networks (WSNs), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. Hence, support for intelligent, autonomous, adaptive and distributed resource management is an essential ingredient of a middleware solution for developing scalable and dynamic WSN applications. In this article, we present a resource management framework based on a two-tier reinforcement learning scheme to enable autonomous self-learning and adaptive applications with inherent support for efficient resource management. Our design goal is to build a system with a bottom-up approach where each sensor node is responsible for its resource allocation and task selection. The first learning tier (micro-learning) allows individual sensor nodes to self-schedule their tasks by using only local information, thus enabling a timely adaptation. The second learning tier (macro-learning) governs the micro-learners by tuning their operating parameters so as to guide the system towards a global application-specific optimization goal (e.g., maximizing the network lifetime). The effectiveness of our framework is exemplified by means of a target tracking application built on top of it. Finally, the performance of our scheme is compared against other existing approaches by simulation. We show that our two-tier reinforcement learning scheme is significantly more efficient than traditional approaches to resource management while fulfilling the application requirements.     

A multi-path cognitive resource management mechanism for QoS provisioning in wireless mesh networks

In a Wireless Mesh Network (WMN), achieving acceptable Quality of Service (QoS) levels requires distributed control over network resources and subsequent awareness of the dynamically changing conditions of the WMN. In this paper, for facilitating such control, a cognitive mechanism is introduced, which facilitates cooperation and cognition among multiple Mesh Access Points and edge routers called Mesh Portals for routing client traffic via multiple paths. The aim of the cognition is to reasonably maximize the fulfillment of the clients from the achieved QoS (e.g., end-to-end delay and bandwidth). The cognitive process consists of three cycles. In the first cycle, the Perception Cycle, the current performance status of the WMN is continuously perceived through feedback loops. The perceived information is further processed and fed into the second cycle, the Learning Cycle, in order to understand the network conditions. This results in the prediction of the performance of the paths and estimation of the path delay for various load conditions. The third cycle, the Decision Cycle, is a game theoretic coalition formation algorithm, that results in path selection and data rate assignment. This algorithm is modeled as a cooperative game theory, which incorporates the Bilateral Shapley Value to find the best coalition from available paths, whereupon a bargaining game theory formulates the data rate assignment. Extensive simulations are performed for evaluating the proposed cognitive mechanism under various load conditions and results demonstrate the evident enhancement of the achieved end-to-end QoS of the clients and the network performance compared with non-cognitive scenarios, specifically in congested conditions.     

Adaptive resource sharing in wireless networks

We propose a state-dependent method for allocating and sharing resources based on the least busy algorithm for a wireless system consisting of several overlaid wireless networks. The model is an approximation that utilizes capacity, reservation and different degrees of mobility of users to characterize the system as a network of queues. We use the network net revenue to compare reservation policies. We show how the use of reservation improves network-wide performance.     

An efficient resource management scheme in light-trail networks

Light-trail, a framework proposed in the past few years, is generalized from the concept of lightpath, and its distinguishing features include bandwidth sharing and efficient bandwidth utilization. Performance of light-trail networks depends on the routing algorithm and the dynamic bandwidth allocation (DBA) scheme, and the former issue has been discussed extensively. In this work, we aim at the design of an efficient DBA scheme, named Demand and Delay-latency Aware with Two-round Deliberation \((\hbox {D}^{2}\hbox {ATD})\), to allocate bandwidth more accurately and efficiently in light-trail networks. In addition to DBA issue, \(\hbox {D}^{2}\hbox {ATD}\) includes a light-trail setup/release mechanism as well. As expected, the simulation results reveal superiority of \(\hbox {D}^{2}\hbox {ATD}\) in both blocking performance and delay performance. Although \(\hbox {D}^{2}\hbox {ATD}\) pays a price of control overhead for performance gain, it is still reasonable since the amount of control messages does not exceed the capacity of the control channel. It verifies that \(\hbox {D}^{2}\hbox {ATD}\) can properly employ the control channel to achieve excellent performance.     

An adaptive FEC scheme for data traffic in wireless ATM networks

A new adaptive forward-error-correction scheme (AFEC) is introduced at the link layer for TCP/IP data traffic in wireless ATM networks. The fading and interference in wireless links cause high and variable error rates, as well as bursty errors. The purpose of the AFEC scheme is to provide a dynamic error-control mechanism by using Reed-Solomon coding to protect the ATM cell payload, as well as the payload type indicator/cell loss priority fields in the ATM cell header. In order to enhance the error tolerance in cell framing and correct delivery, the AFEC scheme functions within a new concept called LANET framing and addressing protection mechanisms. The AFEC scheme has been validated using a simulation testbed of a low-speed wireless ATM network     

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.     

Game-theoretic solutions through intelligent optimization for efficient resource management in wireless visual sensor networks

We propose a quality-driven cross-layer optimization scheme for wireless direct sequence code division multiple access (DS-CDMA) visual sensor networks (VSNs). The scheme takes into account the fact that different nodes image videos with varying amounts of motion and determines the source coding rate, channel coding rate, and power level for each node under constraints on the available bit rate and power. The objective is to maximize the quality of the video received by the centralized control unit (CCU) from each node. However, since increasing the power level of one node will lead to increased interference with the rest of the nodes, simultaneous maximization of the video qualities of all nodes is not possible. In fact, there are an infinite number of Pareto-optimal solutions. Thus, we propose the use of the Nash bargaining solution (NBS), which pinpoints one of the infinite Pareto-optimal solutions, based on the stipulation that the solution should satisfy four fairness axioms. The NBS results in a mixed-integer optimization problem, which is solved using the particle swarm optimization (PSO) algorithm. The presented experimental results demonstrate the advantages of the NBS compared with alternative optimization criteria.     

An incentive energy-efficient routing for data gathering in wireless cooperative networks

Because of energy-constraint, it is an attractive problem to select energy-efficient paths from source nodes to sink for data gathering in wireless ad hoc networks. Cooperative communication is a promising mechanism to reduce transmit energy in such kind of case. One of the fundamental assumptions for cooperative communication is that each node should be unselfish, responsible, and willing to forwarding data he has received. However, in energy-constrained environment, because of limited energy, each node hates participating in data transmission without any incentive and tries to avoid forwarding data (this behavior is selfish). In this paper, a utility function is proposed to stimulate nodes to behave unselfishly. We prove that it is a Nash Equilibrium when nodes work in an unselfish manner. Also, we show that the selection of forwarding nodes and relay nodes for data transmission is a NP-hard problem even when nodes behave unselfishly. A heuristic algorithm (Algorithm for Node Selection Problem, ANSP) is provided to solve this selection problem. We also prove the convergence of this algorithm. The analysis shows that this algorithm can reach the approximate performance ratio of 2?(1+α), where α is the maximal ratio of two power consumptions on two adjacent links in the network. The numerical results show that in a 100 node network, if nodes behave unselfishly, they will obtain a better utility, and more energy will be saved. The average saved energy when each node takes a selfish behavior, is 52.5% less than the average when nodes behave in an unselfish manner.     

Fundamental limitations of power control and radio resource management in wireless networks

The ultimate resource in wireless networks is transmission power. The system stability and the resource sharing algorithms all rely on well functioning power control. To realistically address resource management algorithms, it is important to consider fundamental limitations of the radio resource and of power control. Exact and approximate capacity expressions are derived and related to Quality of Service (QoS) requirements of services. Power control is subject to limited update rate, limited feedback bandwidth, time delays, measurement errors, feedback errors and filtering effects which all affect the resulting performance. Simulations further illustrate the hampering effects and motivates models of power control inaccuracies. Copyright © 2004 John Wiley & Sons, Ltd.     

Energy-efficient resource allocation model for device-to-device communication in 5G wireless personal area networks

In general, there are several many devices that can overload the network and reduce performance. Devices can minimize interference and optimize bandwidth usage by using directional antennas and by avoiding overlapping communication ranges. In addition, devices need to carefully manage their use of resources, such as bandwidth and energy. Bandwidth is limited in wireless personal area networks (WPANs), so devices need to carefully select which data to send and receive. In this paper, an intelligent performance analysis of energy-efficient resource optimization model has been proposed for device-to-device (D2D) communication in fifth-generation (5G) WPAN. The proposed energy-efficient resource allocation in D2D communication is important because it helps reduce energy consumption and extend the lifespan of devices that are communicating with each other. By allocating resources in an efficient manner, communication between two devices can be optimized for maximum efficiency. This helps reduce the amount of energy needed to power the communication, as well as the amount of energy needed to power the device that is communicating with another device. Additionally, efficient resource allocation helps reduce the overall cost of communication, as the use of fewer resources results in a lower overall cost. The proposed efficient resource allocation helps reduce the environmental impact of communication, as less energy is used for communication. The proposed energy-efficient resource allocation model (EERAM) has reached 92.97% of energy allocation, 88.72% of power allocation, 87.79% of bandwidth allocation, 87.93% of spectrum allocation, 88.43% of channel allocation, 25.47% of end-to-end delay, 94.33% of network data speed, and 90.99% of network throughput.     

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.     

An SDN‐based scalable routing and resource management model for service provider networks

Volume of the Internet traffic has increased significantly in recent years. Service providers (SPs) are now striving to make resource management and considering dynamically changing large volume of network traffic. In this context, software defined networking (SDN) has been alluring the attention of SPs, as it provides virtualization, programmability, ease of management, and so on. Yet severe scalability issues are one of the key challenges of the SDN due to its centralized architecture. First of all, SDN controller may become the bottleneck as the number of flows and switches increase. It is because routing and admission control decisions are made per flow basis by the controller. Second, there is a signaling overhead between the controller and switches since the controller makes decisions on behalf of them. In line with the aforementioned explanations, this paper proposes an SDN‐based scalable routing and resource management model (SRRM) for SPs. The proposed model is twofold. SRRM performs routing, admission control, and signaling operations (RASOs) in a scalable manner. Additionally, resource management has also been accomplished to increase link use. To achieve high degree of scalability and resource use, pre‐established paths (PEPs) between each edge node in the domain are provided. The proposed controller performs RASOs based on PEPs. The controller also balances the load of PEPs and adjusts their path capacities dynamically to increase resource use. Experimental results show that SRRM can successfully perform RASOs in a scalable way and also increase link use even under heavy traffic loads.     

An adaptive-weighted two-dimensional data aggregation algorithm for clustered wireless sensor networks

In this paper, an Adaptive-Weighted Time-Dimensional and Space-Dimensional （AWTDSD） data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network. AWTDSD contains three phases： （1） the time-dimensional aggregation phase for eliminating the data redundancy; （2） the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and （3） the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station. AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well. Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.