Summary Social question and answer (Q&A) communities or websites, such as Quora, StackOverflow, become popular from the last decade. They provide collected answers in a crowdsourcing way by encouraging the interactive between users. The ranking of answers in such Q&A websites depends on many factors, such as received upvotes, downvotes, publishing time, and so on. When there exist malicious users, they can manipulate the ranking of answers by launching voting spamming attacks. In this article, we propose a metric, diversity of visibility, to characterize the balance between rankings of answers with conflicting opinions and propose a time‐diversitybased voting scheme to reach a high diversity of visibility, so as to minimize the impact of voting spamming attacks. We evaluate the proposed metric and voting scheme in the case study of a Chinese Q&A website, Zhihu, and show that the proposed voting scheme consistently shows a good balance of answers with conflicting opinions in both actual scenarios and synthetic scenarios.

In vehicular networks, the benefits of jointly-optimizing data prefetching and caching with broadcast transmission scheduling and rate adaptation strategies at road-side units (RSUs) have already been demonstrated in the literature. Nevertheless, the effectiveness of the solution depends greatly on the accurate knowledge of vehicular trajectories. In practice, as specified in the standards, this is at odds with the important issue of privacy. One of the main contributions of this work is to address this issue by providing a scheme that jointly optimizes the throughput of broadcast-transmission scheduling from RSUs to vehicles, and the privacy of vehicles’ locations, by enabling them to disseminate obfuscated locations to the server from time to time. We formulate this problem as a reinforcement learning (RL) problem, where the vehicles learn when to report obfuscated locations, in order to maximize a utility that encompasses both the network capacity (related to vehicles’ throughput) and the level of privacy achieved. The proposed scheme is shown to consistently outperform alternative randomized schemes considered in past work, and in particular it proves to be robust against prediction errors of the future locations of the vehicles.

This paper presents an analytical model for the average Packet Error Rate (PER) of Bluetooth in Basic Rate (BR) mode. The effects of the physical layer and the MAC layer are taken into account. A Nakagami-m block fading is considered to take into account the effects of wireless channel fading. A Gaussian Frequency Shift Keying (GFSK) modulation scheme is used. Also, Forward Error Control (FEC) coding effects are taken into account. The interference between the different piconets is captured by a simple Medium Access Control (MAC) layer collision model. An approximation of instantaneous PER is obtained as a first result. Then, a closed-form expression is derived for the PER at the physical layer of Bluetooth. Finally, the overall average PER at the physical and MAC layer of Bluetooth is computed. Extensive simulations are performed to show the accuracy of the obtained results.

Traditional cache algorithms for information centric networks cache every seen item and rely on simple statistics, such as usage recency, to determine the target for replacement. While they result in a simple and efficient O(1) algorithms, such simple statistics perform poorly when dealing with realistic request patterns, that are non-uniform and that often exhibit daily, weekly and monthly seasonality. As the computational power of processors increased dramatically over the years, it is worth trying to use more complex cache algorithms in order to yield better performance. In this paper, we investigate the use of convolutional neural networks as a means to capture the seasonal patterns in the requests stream, and propose a cache algorithm that is able to consider seasonality and make decisions accordingly. We implement our cache algorithm by building an interface to the real-world ICN forwarder from the CICN open source project, the so-called Metis forwarder; and conduct our experiments on a real-world dataset representing the access statistics of Wikipedia to demonstrate the effectiveness of our algorithm.

Prefetching and caching content at road-side units (RSUs) and broadcast-transmission scheduling can be shown to improve the throughput in vehicular networks considerably, provided vehicular trajectories prediction are accurate enough. As such many studies assume implicitly that such trajectories are accurately available via GPS or location tracking. In practice, as discussed in the standards, this raises a big issue of privacy. In this paper, we focus on jointly optimizing the broadcastscheduling throughput from RSUs to vehicles, while preserving the privacy of vehicles by enabling them to disseminate obfuscated location information to the server. As it is difficult to predict the vehicular throughput according to its disseminated obfuscated locations, we propose to use the capacity based on reported information as an approximation. We formulate the problem as a reinforcement learning (RL) problem, where the decision variables concern the action to obfuscate disseminated location according to current location and the last disseminated location, with the objective of maximizing a utility function that consists of a weighted sum of the capacity and the level of privacy. Simulation results show that, the proposed scheme consistently outperforms the randomized benchmark, and is insensitive to the prediction accuracy of vehicles' future locations.

We propose a simulation model of Bluetooth (BT) passive scanning for vehicular traffic monitoring. The model is designed to simulate packet detection in the physical and Medium Access Control (MAC) layers for fixed BT sensors. At the physical layer, we consider the radio propagation effects of path loss, small- and large-scale fading. At the MAC layer, we model channel hopping and packet collisions. Associated to a vehicular traffic simulator, our proposal provides a cost-efficient way to generate large-scale BT traffic measurement datasets including both sensor and ground-truth data, avoiding deployment costs. We validate the model by comparing the simulation output to experimental data and we show a simulation use-case of vehicular traffic monitoring in a complex urban context.

Prefetching data at the road side units (RSUs) and transmitting them to interested vehicles can help reduce the traffic load in vehicular networks, and improve the data retrieval time. Most prior work in this area opted for supporting data delivery by using a single data rate, in stark contrast to common networking knowledge that good tradeoffs can be achieved in terms of performance by adopting different, more appropriate data rates for different users. In particular, in general wireless networks it is well known that when the data rate is small, the transmission is robust but takes too long; while when the data rate is high, the data delivery time is smaller but the coverage area of the RSU becomes smaller and so does the robustness. In this paper, we study the joint problem of caching and scheduling to decide, what data to prefetch, when to deliver it, and which data rate to use, with the objective of maximizing the volume of data delivered to the mobile vehicles in the network. This problem is subject to many constraints imposed by the real system limits, such as the mobility pattern of the vehicles, the distribution of data requests by the vehicles, and the limited buffer space available at the RSUs. We formulate the problem as an integer linear programming problem, and propose a heuristic caching algorithm, and a heuristic scheduling algorithm to find an approximate solution that is shown via simulation to improve the throughput, compared to standard alternative approaches.

Due to the highly dynamic network topology of Vehicular Ad-hoc Networks (VANETs), an effective clustering algorithm is always required to both solve the scalability problem and enhance vehicles' connections. In this paper, a discrete- time finite-state Markov chain model is proposed based on our previous clustering framework, in order to provide a comprehensive analysis of cluster stability, including cluster's lifetime and cluster member's lifetime. Moreover, the future clustering performance can be predicted through the proposed model. Numerical results are presented to evaluate the model, which show high consistency between analytical and simulation results.

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

In this paper, we present a link duration prediction method in VANETs. It utilizes AdaBoost algorithm to combine several link metrics, such as distance, difference in velocities, link lifetime, and etc., to form a predictor with a higher accuracy. The proposed method is applicable to different traffic scenarios and does not rely on any assumption of vehicles' velocity distribution. We evaluate the performance of this Adaboost-based link duration prediction algorithm by various traffic traces generated by SUMO that represents different typical scenarios. The evaluation result shows that, the proposed method effectively improve link prediction accuracy. Compared with other machine learning based solutions such as linear regression and support vector regression, the proposed method also shows less prediction error consistently.

In the literature, there are many clustering algorithms proposed for the vehicle ad hoc networks (VANETs) to improve network stability and scalability. However, there is a lack of comprehensive comparison among them. In this paper, we show that there exists unfair comparison of clustering algorithms, in the aspect of simulators, performance metrics, simulation scenarios, and configuration of algorithms. To start the first step to tackle this problem, we propose a general framework to Fairly Compare Clustering algorithms in VANETs (FCC). Under this framework, we show that, i) misconfiguration of clustering algorithms can lead to significant performance degradation, ii) there is no winning clustering algorithms in all scenarios, iii) the design of clustering algorithms should be scenario-dependent.

In this paper, we present a data-driven solution to address the energy efficient routing problem in a multi-layer femtocell network. The basic idea is to divide problems into categories by the clustering of traffic demand vectors, and solve a problem by a most suitable algorithm, in terms of throughput, energy consumption and computation time, corresponding to its category. The simulation results show that, our proposed approach can achieve a similar performance in throughput and energy saving as the previous JRN algorithm, by reducing 80% computation time.

Effective clustering algorithms are indispensable in order to solve the scalability problem in Vehicular Ad-hoc Networks (VANETs). Due to the highly dynamic network topology, an effective cluster merging scheme is always required in clustering algorithms, aiming to prevent the collapse of clusters. In the literature, there is a lack of comparison of cluster merging schemes, which makes it hard to analyze the impact of this component on clustering performance. In this paper, we analyze the existing cluster merging schemes and propose a Leadership-based Cluster Merging (LCM) scheme. Then, a comprehensive comparison of different cluster merging schemes under various traffic scenarios is presented, and our scheme is shown to achieve better performance on cluster stability.

Clustering in vehicular ad hoc networks (VANETs) is a challenging issue due to the highly dynamic vehicle mobility and frequent communication disconnections problems. Recent years' research have proven that mobility-based clustering mechanisms considering speed, moving direction, position, destination and density, were more effective in improving cluster stability. In this paper, we propose a new mobility-based and stability-based clustering algorithm (MSCA) for urban city scenario, which makes use of vehicle's moving direction, relative position and link lifetime estimation. We evaluate the performance of our proposed algorithm in terms of changing maximum lane speed and traffic flow rate. Our proposed algorithm performs well in terms of average cluster head lifetime and average number of clusters.

The CDN serves as an essential element in providing content delivery services on the Internet; however, limited by its footprint and influenced by variations of network conditions and user demands, no individual CDN can qualitatively fulfill delivery tasks anytime and anywhere. As such, large content providers often use multiple CDNs. Nevertheless, this approach is not only cumbersome to negotiate multiple business contracts, but also economically inefficient, especially for small content providers. A simpler alternative approach would be to let each content provider only sign one contract with an authoritative CDN, say CDN-0, and let CDN-0 handle the potential performance trap of individual CDN. To fulfill its business commitment, CDN-0 could either use its own infrastructure or rent services from other CDNs, which is sometimes indispensable. To make an optimal operating plan for CDN-0 operator, we propose a new problem, called MCDN-CM, whose objective is to minimize CDN-0's operating cost. MCDN-CM is a concave minimization problem and we take advantage of the special form of the practical CDN-pricing function - piece-wise linear concave - to arrive at an optimal solution through an iterative procedure. We conduct numerical experiments under realistic settings and show via the experimental results that CDN-0 can achieve a tremendous cost-saving by using our proposed algorithm.

We study the problem of information dissemination in vehicular networks in this paper. Crucial information, such as traffic jamming status, is usually broadcasted inside the network. Each vehicle in the network has its own choice to either forward or drop packets for others. Although the energy consumption in general is not an issue in vehicular networks, forwarding every received packets is still not preferred as it may result in congestion in transmission. We model this problem via evolutionary game theory to investigate the cooperative behavior among vehicles. Simulation results show that, the cooperation ratio in the network is proportional to the setting of synergy factor for the evolutionary game. When nodes change their strategies according to the evolutionary game, information can be disseminated as fast as flooding scheme, while more bandwidth can be reserved.

This paper presents a new jamming attack in wireless LANs that deliberately targets uplink TCP acknowledgements (TCP-ACKs) of downlink TCP flows. To ensure immunity to detection with existing schemes, in this attack, the attacker does not jam the target constantly; instead, it relies on our probabilistic estimation model to forecast the time when its transmission has the highest likelihood of colliding with the target's generated TCP-ACKs. Repeating this process results in a decrease of the average congestion window of the targeted due to an increased round-trip time (RTT). The rogue node and/or its colluding attackers can grab this freed bandwidth and increase their throughput. We demonstrate via ns-2 simulation the effectiveness of such attack and show how easy it is to deploy without hardware modification. We also discuss its immunity to detection by existing detection schemes and investigate some parameters that may be used in building future detection mechanisms.

In dense indoor multi-cell wireless networks, such as WLANs and OFDMA-based femtocell networks (e.g., LTE, WiMAX), the wireless bandwidth waste has become more dramatic due to the large interference and contention occasioned by uncoordinated channel access methods such as CSMA. Coordinating resource allocation in the network can be a good compromise solution to this problem. However, in general, resource allocation in wireless networks is a complex multi-dimensional problem that involves four tasks: client association to select a base station to associate with, antenna beam selection when directional antennas are used, link scheduling to ensure conflict freedom, and power adaptation to reduce mutual interference. In this paper we study the joint optimization of the above four components, and propose a unified conflict-free scheduling algorithm that solves the joint problem with two alternative objectives: (1) power-utility-maximization and (2) fair-throughput-maximization. Our scheduling algorithm can be directly implemented in OFDMA-based femtocell networks. In addition, to enable realistic deployment in WLANs, we design the so-called TD-CSMA, a coordinated access protocol that is compatible with the legacy IEEE 802.11 MAC protocol. With extensive simulations in ns-2 we notably show that our model outperforms some benchmark algorithms on a wide range of metrics.

In this work, we investigate upon the potential of considering the Intelligent Transportation Systems (ITS) for cellular traffic offloading as an alternative to Wi-Fi and femtoCells approaches. We propose an analytical study based on an optimization problem formulation called VOPP where the objective is to elect the maximum number of data flows that could be routed to the downloaders through the Vehicular Network (VANET). VOPP considers the link availability between the infrastructure and the downloader, the medium contention, interferences, data volume, gateways capacity and the channel aggregation to evaluate the VANET capacity to support additional traffics. Numerical results of our analytical approach show that the data offloading fraction is closely tied to the data volume of the flows and to the vehicles positions and VANET topology.

Multi-cell wireless LANs (WLANs) are often densely deployed in order to achieve full coverage of a given area, in particular in indoor environments. In such scenarios, it is widely believed that the co-existence of hidden and exposed APs simultaneously results in a dramatic download throughput degradation for the clients. Indeed, in such environment, intutively, each AP while competing for channel access with neighbouring exposed APs may suffer collisions from hidden APs each time it wins such competition. In this paper we study the problem of fairly allocating download bandwidth to clients in such dense multi-cell WLANs. To this end, we design algorithms that adapt client association, airtime sharing among clients and rate control to achieve optimal fairness in bandwidth access. We model this problem as a non-linear programming problem, and propose a distributed algorithm to solve such an NP-hard problem via transformation into a convex problem as well as approximations. Our numerical and simulation results show that, the proposed algorithm significantly improves the performance compared to existing solutions, and leads to the counter intuitive conclusion that intelligently adding exposed APs will lead to better system performance.1

In this paper we study the problem of client association, airtime sharing and contention resolution in a multi-cell WLAN to achieve fair download bandwidth sharing. We first model the problem as a utility maximization problem, then, because of the integral nature of association, the problem being NP-hard, we decompose it into two sub-problems: the client association problem on one hand, and the joint airtime sharing and contention resolution problem on the other. We show that, for each fixed association pattern, the corresponding joint airtime sharing and contention resolution problem can be solved in polynomial time. To solve the client association problem, we propose the so-called Swap localized algorithm. The Swap procedure iteratively swaps clients between pairs of neighbour access points (APs) if the swap leads to an improvement in the global utility. Simulation results show that, the proposed algorithm is significantly better than RSSI-based heuristic, and quickly leads to a near-optimal solution.

In this paper we study the problem of throughput maximization, with fairness guarantee, in multi-cell WLANs with hidden terminals. In such a scenario, the throughput gained by each flow is generally determined by the four major control mechanisms that make the basis of WLAN control protocols - viz., radio frequency channel assignment, association control, intra-AP airtime sharing between the associated clients, and the access rate control which is determined by the MAC layer contention window tuning. Instead of focusing on each of the four mechanisms apart, in this paper we optimize the network performance via a joint channel assignment, association control, airtime sharing, and contention window tuning; we formulate this joint problem as a non-linear programming problem. Because such a problem is in general NP hard due to the integral nature of both channel assignment and association control, we solve it by iteratively approximating it via a convex programming problem. Numerical and simulation results show that, the resulting solution significantly improves the network throughput and fairness, compared with methods that optimize separately the different mechanisms. Our solution also gives further insights into the importance of each one of these mechanisms when considered jointly.

In this paper we study the problem of joint client association, airtime sharing and contention resolution in IEEE802.11 multi-cell WLANs with directional antennas with the goal of achieving optimal fair throughput. We formulate the problem as a non-linear programming problem and solve it under the network utility maximization framework. Using simulation, we demonstrate that i) the association pattern should try to balance the number of clients associated with each AP to use the capacity of more APs; ii) clients in the same transmission range of an antenna should always be associated with the same AP to avoid hidden node collisions.

The wormhole attack is a severe attack in Wireless Mesh Networks (WMNs). It involves two or more wormhole endpoints colluding to capture traffic from one place in the network and replay it to another faraway place through a secret tunnel, so as to distort network routing. It may lead to even more serious threats such as packet dropping and denial of service (DoS). Although a lot of works have been done on detecting wormhole attacks, few of them actually evaluated their solutions on a testbed to consider the real network conditions. In this paper, we set up a WMN testbed for studying wormhole attacks to fill this gap. Some existing approaches used RTT to detect wormhole attacks. However, from both theoretical analysis and experimental results, we observed that the standard deviation of round trip time (stdev(RTT)) is a more efficient metric than RTT to identify wormhole attacks. Accordingly, we propose a new algorithm called Neighbor-Probe-Acknowledge (NPA) to detect wormhole attacks. Compared with existing works, NPA does not need time synchronization or extra hardware support. Moreover, it achieves higher detection rate and lower false alarm rate than the methods using RTT under different background traffic load conditions.

In this article we analyze capacity improvement by directional antennas over omni antennas in wireless sensor networks. The capacity in our analysis is the end-to-end per-node throughput. We analyze the typical traffic pattern for sensor networks, where traffics are destined to or originated from the sink. The main results of our analysis are summarized as follows. (1) The network capacity is O (1/ N ) for both omni and directional antennas, where N is the number of sensor nodes in the network. (2) In the case of line deployment, the capacity ratio of directional antennas over omni antennas is bounded by (2 q +3)/(2 q -1), where q is the ratio of the interference radius to the transmission radius. (3) In the case of two-dimensional deployment, the capacity for using directional antennas is O (1/θ) for m = 2, and O (lg m /θ 2 lg(1/θ)) for m > 2, where m is the number of radios (antennas) on each node and θ is the beamwidth of antennas. (4) When there are n > 1 sinks, the capacity has a non-monotonic relationship with the transmission radius. The optimal transmission radius depends on the ratio of n/q . (5) The capacity ratio of directional antennas over omni antennas in multi-channel networks decreases as the channel number/radio number ratio c/m increases.

In this paper, we investigate the cost-constrained incremental network planning problem in multi-hop wireless networks. Given an existing wireless network, we study the problem of how to add new wireless links to improve the network performance under the deployment cost constraint. After formulating the problem as COst-constrained Incremental Network (COIN) problem, we introduce a performance metric, which represents the tradeoff between throughput improvement and deployment cost. We then propose a Greedy Link Deployment (GLiD) algorithm using the limited-enumeration and greedy searching methods. Finally, through extensive simulations, we evaluate the performance of our algorithm in comparison with other solutions. The results show that our algorithm can perform much better than random deployment and purely greedy deployment methods.

In most of the existing video surveillance systems, captured video streams by CCTV or video cameras are aggregated through cables to a central station and monitored by associated human operators. However, the high deployment cost, low scalability and reuseability still remain to be the main roadblock for their wider application. Using human to monitor events can also be unreliable. Accordingly, we designed a ubiquitous wireless video surveillance system. This system uses wireless sensor nodes to detect pre-defined events and utilizes wireless mesh network to transmit high-quality video streams. Without cables, the deployment cost is significantly decreased. Different application users can also define various events and automatically receive notification and corresponding video streams when the events occur. In addition, mobile users, even when roaming, can access this system. In this demo, we introduce the hardware and software of this system and its implementation in our intelligent transportation system testbed.

The IEEE Media Independent Handover (MIH) services are not aware of different user contexts, and thus cannot provide context-aware services to improve user experience. To address this problem, we extend MIH with a context-awareness module. More specifically, we present a context-aware mechanism for generating timely MIH Link Going Down (LGD) event that triggers handover preparation before losing the current connection. Unlike existing works, which focus on signal strength threshold, our proposed mechanism can guarantee user required handover preparation time. Experiment results show that our mechanism can generate timely MIH LGD event.

This paper proposes a novel design of Publish/Subscribe (Pub/Sub) based system for event-driven video surveillance over wireless networks. Different from the previous Pub/Sub works, the proposed system considers the real-time requirement together with the system scalability. We study the problem of selecting an optimal subset of brokers for subscription forwarding in a large scale delay constrained Pub/Sub system on top of a wireless mesh/ad-hoc network. We prove that the problem can be reduced from a minimum connected K-dominating set problem, which has been proved to be NP-complete. A heuristic algorithm is then proposed to solve the problem and its performance is evaluated by comprehensive simulation. We also share our experience of real-world implementation of the Pub/Sub based video surveillance system.

This paper addresses the AP placement problem in an indoor environment. Given a set of clients (end users) and their locations, each client has a traffic demand for Internet access. An AP can be equipped with one radio, dual radios, or even three radios (all for client access). Each AP can adjust its transmission power according to its local environment. The problem of our concern is to find out the optimal placement of APs such that the total cost of all APs is minimized, subject to the constraint that the traffic demand for each client can be fulfilled. The cost of an AP includes the cost of the AP box and the cost of radios installed on it. We proposed a heuristic algorithm that initially places an AP at each client location and gradually merges two neighboring APs into one AP until no further merging can be made due to bandwidth constraint. Simulation results show the heuristic algorithm can significantly reduce the total APs' cost, compared with other heuristic methods.

There are two widely used interference models to characterize interference in wireless communication, protocol model and physical model. The protocol model simplifies the interference by considering only two concerned communication links (or nodes). It ignores the cumulative interference from other links (or nodes). On the other side, the physical model considers the cumulative interference of the ambiance, but its application is very much limited due to the complexity to compute the physical interference of the whole system. In this paper, we study the AP placement problem in the physical interference model. By assuming a simple scheduling method, we propose a heuristic algorithm aiming to find the placement of minimal number of APs in an indoor region to meet the end users' QoS requirements. From the simulation results, we find that there is a significant difference in performance between the algorithms that use the physical model and the protocol model. It shows that the results obtained from AP placement algorithm in the protocol model are not close to the real situation due to neglect of cumulative interference.

We investigate the problem of delay constrained maximal information collection for CSMA-based wireless sensor networks. We study how to allocate the maximal allowable transmission delay at each node, such that the amount of information collected at the sink is maximized and the total delay for the data aggregation is within the given bound. We formulate the problem by using dynamic programming and propose an optimal algorithm for the optimal assignment of transmission attempts. Based on the analysis of the optimal solution, we propose a distributed greedy algorithm. It is shown to have a similar performance as the optimal one.

In this paper, we study topology control methods aiming at achieving high throughput for wireless mesh networks. Based on the analysis of the existing topology control methods, such as the shortest path tree method, the load balancing method and the greedy method, we propose a relative-closest connect-first topology control algorithm. In our proposed method, each node has a distance vector to keep the hop-distance to all gateway nodes, one component for each gateway. A node that has a closest relative distance to a gateway has the highest priority to be connected to the subtree rooted from the gateway. Our proposed topology control method combines the merits of the shortest path tree, the load balancing and the greedy methods. We also present a distributed implementation of the proposed topology control method. Simulation results have shown our proposed method achieves better performance than the existing methods under various distributions of nodes and gateways, and the distributed method achieves a similar performance to the centralized one.

In this paper we proposed a topology control algorithm for the single channel wireless mesh network with m directional antennas equipped on each node. The objective of our algorithm is to maximize the traffic delivery ratio from each non-gateway node to gateway nodes, by adjusting the orientations of the directional antennas appropriately. By allowing each antenna connecting to multiple nodes, our proposed algorithm improves traffic delivery ratio by 40%~280%, compared to previous work.

In this paper we analyze the capacity of wireless mesh networks that use omni or directional antennas. The capacity in our analysis is the end-to-end per-node throughput. Our analysis is based on the assumption that there is only one gateway in the network and all end-users' traffics go through the gateway. Non-gateway nodes are uniformly distributed in a two- dimensional region centered at the gateway. The main results of our analysis can be summarized as: 1) The capacity is O(1/N) for both omni and directional antennas, where N is number of nodes in the network. 2) the capacity is O(1g m/thetas) for m = 2, and O(1g m/thetas21g (1/thetas)) for m > 2, where m is the number of antennas on each node, and thetas is the beamwidth of antennas.

In this paper we propose a topology control algorithm for the wireless mesh network, where each node is equipped with m non-steerable directional antennas. Our objective is to maximize the minimal traffic delivery ratio from each node to the gateway nodes, by adjusting the orientation of the directional antennas appropriately. We show by simulations that our proposed algorithm improves traffic delivery ratio significantly, compared with the random network. Moreover, we study how each parameter, such as the beam width of antennas, the number of antennas on each node, has impact on the network performance.

In this paper we study the correlations that exist between the required transmission power of RTS, CTS, DATA and ACK frames to guarantee a successful 4-way handshake in wireless ad hoc networks based on the IEEE 802.11 distributed coordination function (DCF). From these correlations, and starting from a set of different initial conditions, we can derive a class of power controlled media access control (MAC) algorithms for single channel ad hoc networks based on the existing IEEE 802.11 DCF. From substantial simulations of the different algorithms and comparison to some prominent alternatives, we come to a conclusion that contradicts the intuitively sound, commonly held belief which states that sending control frames at maximum power - to reduce interference on the long data frames - then, the ensuing long data frames at a lower, yet sufficient power level decreases power consumption per carried bit.

Power control for mobile ad hoc networks has received increasing interest from the research community. Power control can be invoked to achieve different objectives, including the most popular goals of either increasing the network throughput by mitigating the effects of interference on a node or ensuring minimal power consumption. We propose a power control algorithm for ad hoc networks to improve network throughput. As such, we study and exploit the correlation that exists between the appropriate transmit power of successive RTS, CTS, DATA and ACK frames and propose an adaptive power control algorithm in the framework of the IEEE 802.11 MAC protocol. In contrast to some alternative approaches, our algorithm operates in a single channel and does not require additional hardware support, which makes it very appropriate for existing IEEE 802.11 based networks.

In this paper an adaptive opportunistic scheduling scheme is proposed for wireless LANs in the presence of a link adaptation scheme such as auto-rate fall-back (ARF). Extensive simulation experiments and scenarios have shown that this scheme outperforms other alternatives in terms of throughput as well as adaptation to different channel characteristics such as location dependent and bursty errors. Current work consists in implementing this algorithm in a real test-bed using IEEE802.11a/g wireless LAN access point.

Power control is often invoked in mobile ad hoc networks to achieve one of two popular goals: i) increasing network throughput by mitigating the effects of mutual interference of nodes or; ii) ensuring minimal power consumption. This paper proposes a new adaptive power control algorithm for ad hoc networks to save energy while maintaining a similar throughput as that achieved by the IEEE 802.11 MAC protocol. The contributions of this proposed scheme are two fold. First, we define and invoke the concept of MAC packet delivery curve to guide the power control algorithm in the adaptation decision under different possible scenarios. In particular when choosing among different service profiles the packet curve can tell which is better in the long term. Then, the power control algorithm takes into consideration the short term correlations between transmit powers of RTS/CTS frames and those of DATA/ACKs frames, to ensure robustness.