Showing papers in "Wireless Networks in 2013"

Directional routing and scheduling for green vehicular delay tolerant networks

Abstract: The vehicle delay tolerant networks (DTNs) make opportunistic communications by utilizing the mobility of vehicles, where the node makes delay-tolerant based "carry and forward" mechanism to deliver the packets. The routing schemes for vehicle networks are challenging for varied network environment. Most of the existing DTN routing including routing for vehicular DTNs mainly focus on metrics such as delay, hop count and bandwidth, etc. A new focus in green communications is with the goal of saving energy by optimizing network performance and ultimately protecting the natural climate. The energy---efficient communication schemes designed for vehicular networks are imminent because of the pollution, energy consumption and heat dissipation. In this paper, we present a directional routing and scheduling scheme (DRSS) for green vehicle DTNs by using Nash Q-learning approach that can optimize the energy efficiency with the considerations of congestion, buffer and delay. Our scheme solves the routing and scheduling problem as a learning process by geographic routing and flow control toward the optimal direction. To speed up the learning process, our scheme uses a hybrid method with forwarding and replication according to traffic pattern. The DRSS algorithm explores the possible strategies, and then exploits the knowledge obtained to adapt its strategy and achieve the desired overall objective when considering the stochastic non-cooperative game in on-line multi-commodity routing situations. The simulation results of a vehicular DTN with predetermined mobility model show DRSS achieves good energy efficiency with learning ability, which can guarantee the delivery ratio within the delay bound.

Toward a secure batch verification with group testing for VANET

Abstract: Vehicular Ad-Hoc Network (VANET) is an application of Ad-Hoc Network, which can significantly improve the efficiency of transportation systems. The authentication of information is particularly important in the VANET system, because of its significant impact, and the transportation systems may be paralyzed as a result of receiving the wrong traffic information. Hence, a lot of schemes have been proposed to verify the information of VANET. However, most of currently known schemes verify the information on a one by one basis. In real situation, the large amount of traffic flow will generate a lot of information at the same time. If the authentication method is authenticating one by one, it is bound to lead to information delays, and the system will have difficulty to achieve real-time performance. Therefore, we shall propose an improved authentication of the batch scheme based on bilinear pairing to make VANET more secure, efficient, and more suitable for practical use.

Stochastic geometry based models for modeling cellular networks in urban areas

Abstract: Recently a new approach to modeling cellular networks has been proposed based on the Poisson point process (PPP). Unlike the traditional, popular hexagonal grid model for the locations of base stations, the PPP model is tractable. It has been shown by Andrews et al. (in IEEE Trans Commun 59(11):3122---3134, 2011) that the hexagonal grid model provides upper bounds of the coverage probability while the PPP model gives lower bounds. In this paper, we perform a comprehensive comparison of the PPP and the hexagonal grid models with real base station deployments in urban areas worldwide provided by the open source project OpenCellID. Our simulations show that the PPP model gives upper bounds of the coverage probabilities for urban areas and is more accurate than the hexagonal grid model. In addition, we show that the Poisson cluster process is able to accurately model the base station location distribution.

Decentralised learning MACs for collision-free access in WLANs

Abstract: By combining the features of CSMA and TDMA, fully decentralised WLAN MAC schemes have recently been proposed that converge to collision-free schedules. In this paper we describe a MAC with optimal long-run throughput that is almost decentralised. We then design two schemes that are practically realisable, decentralised approximations of this optimal scheme and operate with different amounts of sensing information. We achieve this by (1) introducing learning algorithms that can substantially speed up convergence to collision free operation; (2) developing a decentralised schedule length adaptation scheme that provides long-run fair (uniform) access to the medium while maintaining collision-free access for arbitrary numbers of stations.

Analytical models for understanding space, backoff, and flow correlation in CSMA wireless networks

Abstract: In wireless networks employing carrier-sense multiple-access with collision avoidance (CSMA/CA), correlations between the service processes of different nodes arise as a result of competition for common wireless channels and the dependencies between upstream and downstream traffic flows. These dependencies make the development of tractable performance models extremely difficult. To address this purpose, we present a new continuous-time model for CSMA wireless networks where we combine a node model and a channel model in order to capture correlation. Simplification methods are presented that make our models computationally tractable for large networks with minimal loss of accuracy. The model can be used for both single and multi-hop wireless networks and takes into account non-saturated queues, backoff-stage dependence of collision probabilities, and the correlation between departure processes and arrival processes of adjacent nodes. The model can be used to compute probabilistic quality of service guarantees to optimize end-to-end throughput and end-to-end delay by adjusting arrival and backoff rates along various paths.

Distributed Hash table-based routing and data management in wireless sensor networks: a survey

Abstract: Recent advances in Wireless Sensor Networks (WSN) have led to a great breakthrough in sensors design and features. These technological novelties have brought additional challenges to WSN. Sensornets are seeking for new approaches for efficient data routing and management. The last few years have witnessed the emergence of several approaches that build Distributed Hash Tables (DHTs) over WSN. DHTs are initially conceived for efficient data lookup in large-scale wired networks. The main objective of this combination is to manage location-independent data and nodes identification. DHT mapping over WSN brings however new challenges. This paper presents an analytical survey on applying DHT techniques in WSNs. It describes existing DHT-based routing and data management protocols and includes a detailed classification of them.

Intelligent beaconless geographical forwarding for urban vehicular environments

Abstract: A Vehicular Ad hoc Network is a type of wireless ad hoc network that facilitates ubiquitous connectivity between vehicles in the absence of fixed infrastructure. Source based geographical routing has been proven to perform well in unstable vehicular networks. However, these routing protocols leverage beacon messages to update the positional information of all direct neighbour nodes. As a result, high channel congestion or problems with outdated neighbour lists may occur. To this end, we propose a street-aware, Intelligent Beaconless (IB) geographical forwarding protocol based on modified 802.11 Request To Send (RTS)/ Clear To Send frames, for urban vehicular networks. That is, at the intersection, each candidate junction node leverage digital road maps as well as distance to destination, power signal strength of the RTS frame and direction routing metrics to determine if it should elect itself as a next relay node. For packet forwarding between Intersections, on the other hand, the candidate node considers the relative direction to the packet carrier node and power signal strength of the RTS frame as routing metrics to elect itself based on intelligently combined metrics. After designing the IB protocol, we implemented it and compared it with standard protocols. The simulation results show that the proposed protocol can improve average delay and successful packet delivery ratio in realistic wireless channel conditions and urban vehicular scenarios.

Evaluating routing metric composition approaches for QoS differentiation in low power and lossy networks

Abstract: The use of Wireless Sensor Networks (WSN) in a wide variety of application domains has been intensively pursued lately while Future Internet designers consider WSN as a network architecture paradigm that provides abundant real-life real-time information which can be exploited to enhance the user experience. The wealth of applications running on WSNs imposes different Quality of Service requirements on the underlying network with respect to delay, reliability and loss. At the same time, WSNs present intricacies such as limited energy, node and network resources. To meet the application's requirements while respecting the characteristics and limitations of the WSN, appropriate routing metrics have to be adopted by the routing protocol. These metrics can be primary (e.g. expected transmission count) to capture a specific effect (e.g. link reliability) and achieve a specific goal (e.g. low number of retransmissions to economize resources) or composite (e.g. combining latency with remaining energy) to satisfy different applications needs and WSNs requirements (e.g. low latency and energy consumption at the same time). In this paper, (a) we specify primary routing metrics and ways to combine them into composite routing metrics, (b) we prove (based on the routing algebra formalism) that these metrics can be utilized in such a way that the routing protocol converges to optimal paths in a loop-free manner and (c) we apply the proposed approach to the RPL protocol specified by the ROLL group of IETF for such low power and lossy link networks to quantify the achieved performance through extensive computer simulations.

On providing location privacy for mobile sinks in wireless sensor networks

Abstract: A common practice in sensor networks is to collect sensing data and report them to the sinks or to some pre-defined data rendezvous points via multi-hop communications. Attackers may locate the sink easily by reading the destination field in the packet header or predicting the arrival of the sink at the rendezvous points, which opens up vulnerabilities to location privacy of the sinks. In this paper, we propose a random data collection scheme to protect the location privacy of mobile sinks in wireless sensor networks. Data are forwarded along random paths and stored at the intermediate nodes probabilistically in the network. The sinks will move around randomly to collect data from the local nodes occasionally, which prevents the attackers from predicting their locations and movements. We analyze different kind of attacks threatening the location privacy of the sinks in sensor networks. We also evaluate the delivery rate, data collection delay and protection strength of our scheme by both analysis and simulations. Both analytical and simulation results show that our scheme can protect location privacy of mobile sinks effectively, while providing satisfactory data collection services.

SAS-TDMA: a source aware scheduling algorithm for real-time communication in industrial wireless sensor networks

Abstract: Scheduling algorithms play an important role for TDMA-based wireless sensor networks. Existing TDMA scheduling algorithms address a multitude of objectives. However, their adaptation to the dynamics of a realistic wireless sensor network has not been investigated in a satisfactory manner. This is a key issue considering the challenges within industrial applications for wireless sensor networks, given the time-constraints and harsh environments. In response to those challenges, we present SAS-TDMA, a source-aware scheduling algorithm. It is a cross-layer solution which adapts itself to network dynamics. It realizes a trade-off between scheduling length and its configurational overhead incurred by rapid responses to routes changes. We implemented a TDMA stack instead of the default CSMA stack and introduced a cross-layer for scheduling in TOSSIM, the TinyOS simulator. Numerical results show that SAS-TDMA improves the quality of service for the entire network. It achieves significant improvements for realistic dynamic wireless sensor networks when compared to existing scheduling algorithms with the aim to minimize latency for real-time communication.

On reducing delay in mobile data collection based wireless sensor networks

Abstract: In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Abstract: Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

Adaptive proportional fairness resource allocation for OFDM-based cognitive radio networks

Abstract: In this paper, we study the resource allocation problem in multiuser Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive radio networks. The interference introduced to Primary Users (PUs) is fully considered, as well as a set of proportional rate constraints to ensure fairness among Secondary Users (SUs). Since it is extremely computationally complex to obtain the optimal solution because of integer constraints, we adopt a two-step method to address the formulated problem. Firstly, a heuristic subchannel assignment is developed based on the normalized capacity of each OFDM subchannel by jointly considering channel gain and the interference to PUs, which approaches a rough proportional fairness and removes the intractable integer constraints. Secondly, for a given subchannel assignment, we derive a fast optimal power distribution algorithm that has a complexity of O(L 2 N) by exploiting the problem's structure, which is much lower than standard convex optimization techniques that generally have a complexity of O((N + K)3), where N, L and K are the number of subchannels, PUs and SUs, respectively. We also develop a simple power distribution algorithm with complexity of only O(L + N), while achieving above 90 % sum capacity of the upper bound. Experiments show that our proposed algorithms work quite well in practical wireless scenarios. A significant capacity gain is obtained and the proportional fairness is satisfied perfectly.

Broadcasting in multi-radio multi-channel wireless networks using simplicial complexes

Abstract: We consider the broadcasting problem in multi-radio multi-channel ad hoc networks. The objective is to minimize the total cost of the network-wide broadcast, where the cost can be of any form that is summable over all the transmissions (e.g., the transmission and reception energy, the price for accessing a specific channel). Our technical approach is based on a simplicial complex model that allows us to capture the broadcast nature of the wireless medium and the heterogeneity across radios and channels. Specifically, we show that broadcasting in multi-radio multi-channel ad hoc networks can be formulated as a minimum spanning problem in simplicial complexes. We establish the NP-completeness of the minimum spanning problem and propose two approximation algorithms with order-optimal performance guarantee. The first approximation algorithm converts the minimum spanning problem in simplical complexes to a minimum connected set cover (MCSC) problem. The second algorithm converts it to a node-weighted Steiner tree problem under the classic graph model. These two algorithms offer tradeoffs between performance and time-complexity. In a broader context, this work appears to be the first that studies the minimum spanning problem in simplicial complexes and weighted MCSC problem.

Reinforcement learning for cooperative sensing gain in cognitive radio ad hoc networks

Abstract: Spectrum sensing is a fundamental function in cognitive radio networks for detecting the presence of primary users in licensed bands. The detection performance may be considerably compromised due to multipath fading and shadowing. To resolve this issue, cooperative sensing is an effective approach to combat channel impairments by cooperation of secondary users. This approach, however, incurs overhead such as delay for reporting local decisions and the increase of control traffic. In this paper, a reinforcement learning-based cooperative sensing (RLCS) method is proposed to address the cooperation overhead problem and improve cooperative gain in cognitive radio ad hoc networks. The proposed algorithm is proven to converge and capable of (1) finding the optimal set of cooperating neighbors with minimum control traffic, (2) minimizings the overall cooperative sensing delay, (3) selecting independent users for cooperation under correlated shadowing, and (4) excluding unreliable users and data from cooperation. Simulation results show that the RLCS method reduces the overhead of cooperative sensing while effectively improving the detection performance to combat correlated shadowing. Moreover, it adapts to environmental change and maintains comparable performance under the impact of primary user activity, user movement, user reliability, and control channel fading.

Energy-efficient and reliable data delivery in wireless sensor networks

Abstract: Clustering has been used as one of energy-efficient mechanisms for data routing in wireless sensor networks. In hierarchical routing approaches, cluster heads are responsible for management (e.g. data aggregation, queries dispatch) and transmission of the collected data in the region controlled by them. For efficient data delivery, several researches have proposed various mechanisms for cluster organization and cluster head selection. However, less focus is given in the area of data transmission associated with Base Station (BS). In such a situation, any failure or packet loss may lead to considerable packet loss. For solving this problem, we propose an efficient data routing scheme for controlling data delivery from nodes to BS. In our proposed approach every node is aware about the link quality of all nodes and is able to deliver data to the BS through the most reliable and energy-efficient route.

Passive cluster-based multipath routing protocol for wireless sensor networks

Abstract: Energy efficiency and quality of service (QoS) are both essential issues in the applications of wireless sensor networks (WSNs) all along, which are mainly reflected in the development of routing and MAC protocols. However, there is little design for achieving the dual performances simultaneously. In this paper, we develop a practical passive cluster-based node-disjoint many to one multipath routing protocol to satisfy the requirements of energy efficiency and QoS in practical WSNs. Passive clustering approach is put to use in the first round, while active clustering technique is taken in the other rounds. Implementation of smart delay strategy makes the cluster distribute uniformly, as well as lessen the number of nodes that have taken part in routing. Among cluster heads, a node-disjoint many to one multipath routing discovery algorithm, which is composed of the optimal path searching process and multipath expansion process, is implemented to find multiple paths at the minimum cost. The simulation results show the proposed protocol achieved very good performance both in energy efficiency and QoS.

On the fairness of resource allocation in wireless mesh networks: a survey

Abstract: This article presents a comprehensive survey of resource allocation techniques in Wireless Mesh Networks (WMNs). Wireless mesh networks have emerged as a key technology for next generation application specific multi-hop wireless networks. We analyze the state-of-the-art resource allocation schemes for WMNs, providing comprehensive taxonomy of the latest work and the future research trends in this field. In general, the resources that are available for WMNs include time, frequency, space, relays, and power. An efficient utilization of these resources can make the network more robust, reliable, and fair. We categorize the resource allocation into "radio" resource allocation, "physical" resource allocation, "utility" based resource optimization, and "cross layer" resource optimization. An ample review of resource allocation schemes within these categories is provided.

Performance of DTN protocols in space communications

Abstract: Delay/disruption tolerant networking (DTN) was developed to enable automated network communications despite the long link delay and frequent link disruptions that generally characterize space communications. The performance of DTN convergence layer adapter (CLA) protocols over asymmetric space communication channels has not yet been comprehensively characterized. In this paper, we present an experimental performance evaluation of DTN CLA protocols for reliable data transport over a space communication infrastructure involving asymmetric channel rates, with particular attention to the recently developed Licklider transmission protocol (LTP) CLA (i.e., LTPCL). The performance of LTPCL is evaluated in comparison with other two reliable CLAs, TCP CLA and a hybrid of TCP CLA and LTPCL, for long-delay cislunar communications in the presence of highly asymmetric channel rates. LTPCL is also evaluated and analyzed in a deep-space communication scenario characterized by a very long link delay and lengthy link disruptions.

Algorithms for finding best locations of cluster heads for minimizing energy consumption in wireless sensor networks

Abstract: Clustering is a widely adopted energy-saving technique in wireless sensor networks (WSNs) In this paper, we study algorithms for finding the best locations of cluster heads in WSNs to minimize the overall energy consumption Specifically, based on the assumption that the global information of all the sensors’ locations or location distribution is available, algorithms are proposed for finding (1) the best location of the cluster head in a single given cluster; (2) the best formation of a given number of clusters where each cluster head has to communicate with base station directly; and (3) the best formation of a given number of clusters where there can be ad-hoc transmission between cluster heads, respectively For each case, algorithms are designed for free-space and multipath energy consumption models respectively Theoretical analysis and extensive simulation results show that the proposed algorithms can steadily and quickly achieve satisfactory results The calculation results of the proposed algorithms provide a useful benchmark for evaluating various local information-based distributed clustering schemes or schemes based on partial or inaccurate global information

Performance analysis of dynamic spectrum handoff scheme with variable bandwidth demand of secondary users for cognitive radio networks

Abstract: Cognitive radio (CR) has attracted considerable attention as a promising technology for solving the current inefficient use of spectrum. In CR networks, available sub-channels are dynamically assigned to secondary users (SUs). However, when a primary user accesses a primary channel consisting of multiple sub-channels, data transmissions of the SUs already using the sub-channels may be terminated. In this paper, we analyze the performance of dynamic spectrum handoff scheme with channel bonding, in which the number of sub-channels used by an SU are variable. We model the multichannel CR network as a multiserver priority queueing system without waiting facility, deriving the blocking probability, the forced termination probability and the throughput for SUs. In terms of the way of forced termination, we consider two policies; one is that SUs using the largest number of sub-channels are forced to terminate their transmissions, and the other is that SUs using the smallest number of sub-channels are chosen for termination. The analysis is also validated by simulation. Numerical examples show that in both forced-termination policies, the throughput of SUs that are forced to terminate their transmissions degrades as the offered load to the system increases.

A QoE handover architecture for converged heterogeneous wireless networks

Abstract: The convergence of real-time multimedia applications, the increasing coverage of heterogeneous wireless networks and the ever-growing popularity of mobile devices are leading to an era of mobile human-centric multimedia services. In this scenario, heterogeneous communications will co-exist and ensure that the end-user is always best connected. The rigorous networking demands of wireless multimedia systems, beyond quality-oriented control strategies, are necessary to guarantee the best user experience over time. Therefore, the Quality of Experience (QoE) support, especially for 2D or 3D videos in multi-operator environments, remains a significant challenge and is crucial for the success of multimedia systems. This paper proposes a QoE Handover Architecture for Converged Heterogeneous Wireless Networks, called QoEHand. QoEHand extends the Media Independent Handover (MIH)/IEEE 802.21 with QoE-awareness, seamless mobility and video adaptation by integrating a set of QoE-based decision-making modules into MIH, namely a video quality estimator, a dynamic class of service mapping and content adaptation schemes. The QoEHand video estimator, mapping and adaptation components operate by coordinating information about video characteristics, available wireless resources in IEEE 802.11e and IEEE 802.16e service classes, and QoE-aware human experience. The video quality estimator works without the need for any decoding, which saves time and minimises processing overheads. Simulations were carried out to show the benefits of QoEHand and its impact on user perception by using objective and subjective QoE metrics.

Reinforcement learning based routing in wireless mesh networks

Abstract: This paper addresses the problem of efficient routing in backbone wireless mesh networks (WMNs) where each mesh router is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count and interference ratio. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously and considers traffic congestion around the gateways is needed. In this paper, we propose an adaptive scheme for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR), that (1) considers the critical areas around the gateways where mesh routers are much more likely to become congested and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio, load at the gateways and end-to end delay. Simulation results show that RLBDR can significantly improve the overall network performance compared to schemes using either Metric of Interference and Channel switching, Best Path to Best Gateway, Expected Transmission count, nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.

A joint parametric prediction model for wireless internet traffic using Hidden Markov Model

Abstract: Addressing performance related issues of networks and ensuring better Quality of Service (QoS) for end-users calls for simple, tractable and realistic traffic models. The work reported here focuses on modelling the Wireless Internet traffic using realistic traffic traces collected over wireless networks and forecasting the end-to-end QoS parameters for the networks. A measurement framework is set-up to collect the QoS parameters and a traffic model is designed based on Hidden Markov Model considering joint distribution of End to End Delay (E2ED or d), Inter-Packet Delay Variation (IPDV) and Packet Size. States are mapped to the four traffic classes namely conversational, streaming, interactive, and background. The model is validated by forecasting QoS parameters and the results are shown to be within the tolerance limit.

Approximation algorithms for deployment of sensors for line segment coverage in wireless sensor networks

Abstract: The coverage problem in wireless sensor networks deals with the problem of covering a region or parts of it with sensors. In this paper, we address the problem of covering a set of line segments with minimum number of sensors. A line segment ? is said to be 1-covered if it intersects the sensing region of at least one among the sensors distributed in a bounded rectangular region R. We assume that the sensing radius of each sensor is uniform. The problem of finding the minimum number of sensors needed to 1-cover each member in a given set of line segments in R is NP-hard. We propose two constant factor approximation algorithms and a PTAS (polynomial time approximation scheme) for the problem for 1-covering a set of axis-parallel line segments. We also show that a PTAS exists for 1-covering a set of arbitrarily oriented line segments in R where the lengths of the line segments are bounded within a constant factor of the sensing radius of each sensor. Finally, we propose a constant factor approximation algorithm for k-covering axis-parallel line segments such that sensors maintain a minimum separation among them.

Queue management based duty cycle control for end-to-end delay guarantees in wireless sensor networks

Abstract: In this paper, we propose an analytical method for duty cycle adaptation in wireless sensor networks so that delay requirement is guaranteed while power consumption is minimized. The proposed method, named Dual-QCon, provides a formal method for stabilizing controller design based on queue management in order to control both duty cycle and queue threshold according to changing network conditions. Dual-QCon also provides a delay notification mechanism in order to determine an appropriate queue threshold of each node according to the application-dependent and time-varying delay requirements. Based on control theory, we analyze the adaptive behavior of the proposed method and derive conditions for system stability. Asymptotic analysis shows that Dual-QCon guarantees end-to-end delay requirement by controlling parameters of local nodes. Simulation results indicate that Dual-QCon outperforms existing scheduling protocols in terms of delay and power consumption.

A token circulation scheme for code assignment and cooperative transmission scheduling in CDMA-based UAV ad hoc networks

Abstract: Code division multiple access (CDMA) ad hoc networks have been considered a promising multiple-channel networking architecture for connecting tactical platforms in battle fields. In this paper we consider a network of a swarm of unmanned aerial vehicles (UAVs) that are used in a tactical surveillance mission. The UAVs are assumed to have multiuser detection capability and form a CDMA-based ad hoc network. A token circulation scheme is proposed to conduct functions required at the medium access control layer including detection of hidden/lost neighbors, code assignment and schedule-based cooperative transmission scheduling. In the proposed scheme, a token continuously circulates around the network based on the "receive-forward" module. Through circulation of the token, each UAV can detect its hidden and/or lost neighbors in near real-time, assign codes enabling the spatial reuse of code channels without incurring code collision, and schedule data transmissions in a cooperative and distributed manner. In addition, the proposed scheme is able to take advantage of multiuser detection functionality and allows for simultaneous transmissions from multiple transmitters to a same receiver. The performance of the proposed token circulation scheme is evaluated, both analytically and through simulations. It is shown that the latency of the token is at most linearly proportional to the network size, and the average delay of a data packet increases with either the packet generation rate or the network size. The results also show that the proposed token circulation scheme is suitable for large-scale CDMA-based UAV ad hoc networks with even heavy network traffic load.

Base station association schemes to reduce unnecessary handovers using location awareness in femtocell networks

Abstract: In this paper, we propose a predictive scheme to generate reduced neighbor lists. In addition, we formulate a predictive association game. The game is designed to determine an optimal base station for maximum throughput and reduced handover frequency. Several simulation scenarios are set forth herein for analysis to show how our schemes improve performance in terms of throughput and handover frequency.

MIMO solution for performance improvements of OFDM---CDMA system with pilot tone

Abstract: In this paper, MIMO OFDM---CDMA downlink scheme is proposed as a solution for improving performance of the OFDM---CDMA downlink system with pilot tone and threshold detection combining (optimum TDC). The new presented system with MIMO included uses space---time block coding applied to two, three and four transmit antennas and it has an arbitrary number of receive antennas. Bit error rate performance in the case of Ricean frequency selective fading is evaluated for the original system as well as for the one with MIMO included. For that reason an adequate simulation model is developed. The results show that the proposed system significantly outperforms the OFDM---CDMA downlink system with pilot tone and optimum TDC.

Divided two-part adaptive intrusion detection system

Abstract: The main objective of this paper is to design a more complete intrusion detection system solution. The paper presents an efficient approach for reducing the rate of alerts using divided two-part adaptive intrusion detection system (DTPAIDS). The proposed DTPAIDS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. The proposed DTPAIDS is designed with the aim of reducing the rate of detected false positive intrusion through two achievements. The first achievement is done by implementing adaptive self-learning neural network in the proposed DTPAIDS to gives it the ability to be automatic adaptively system based on Radial Basis Functions (RBF) neural network. The second achievement is done through dividing the proposed intrusion detection system IDS into two parts. The first part is IDS1, which is installed in the front of firewall and responsible for checking each entry user's packet and deciding if the packet considered is an attack or not. The second is IDS2, which is installed behind the firewall and responsible for detecting only the attacks which passed the firewall. This proposed approach for IDS exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results show that the proposed DTPAIDS [1] reduce false positive rate, [2] detects intrusion occurrence sensitively and precisely, [3] accurately self---adapts diagnoser model, thus improving its detection accuracy.