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Due to the emergence of mobile 3D devices, multi-view 3D videos are expected to play increasingly important roles in providing immersively interactive experiences to users. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires a larger storage space and bandwidth consumption. Nevertheless, efficient caching for multi-view 3D videos in a mobile proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D videos. We then formulate a new cache management problem, named Adaptive View Selection and Cache Operation (AVSCO), and find the optimal policy based on Markov Decision Process (MDP). Afterward, we propose an online algorithm with a guaranteed competitive ratio to support human visual continuity in AVSCO. Then, we devise an approximate MDP to accelerate the computation of MDP by aggregating similar states to reduce the state space. Simulation and prototype implementation results manifest that the proposed algorithms can significantly improve the cache hit rate and reduce the bandwidth consumption for the remote access compared with the existing cache replacement algorithms. [ABSTRACT FROM AUTHOR]

Fog computing is promising for Industrial Internet of Things (IIoT) which are typically large-scale, time-critical, and geo-distributed. However, it needs to address challenges such as scalability and heterogeneity in application provisioning with diverse and strict Quality-of-Service (QoS) requirements from IIoT devices. In this paper, a fog service orchestrator, Q-FSO, is proposed for IIoT application provisioning in which request satisfaction is maximized. With a two-level QoS model that considers key performance metrics for IIoT, including availability, reliability, response time, and cost, Q-FSO can realize large-scale service orchestration by exploiting the advantages of fog computing. Further with a harmonious local QoS assignment model, Q-FSO solves the workflow construction problem by multiple independent local optimization tasks, one for each stage, for the concurrent requests. Each local optimization task can be reduced from the multiple multidimensional knapsack (MMKP) problem. We then propose two practical MMKP heuristic algorithms, namely, Incremental Similarity Matching (ISM) and Greedy Multiple Matching (GMM), to tackle the local QoS assignment problem with polynomial time complexity. The scalability issue in orchestration is then handled by a decentralized workflow construction protocol. The simulation results validate their applicability, demonstrating that Q-FSO is indeed an efficient and effective IIoT application provision mechanism with significant performance benefits in terms of service processing throughput, resource utilization, and speed of service orchestration. [ABSTRACT FROM AUTHOR]

Proof of Work (PoW) is one of the key consensus mechanisms for blockchain systems to ensure trust and secure service. The PoW process, which is also known as mining, is computationally intensive, because the miners need to solve a well-crafted puzzle to add a new block to the chain. With cryptocurrency such as Bitcoin becoming more popular, it requires greater levels of mining power for successful mining. Most miners tend to join specific mining pools to share rewards contributed by their mining power. This leads to more centralized PoWbased blockchain systems, which contradicts the design goal of decentralization for blockchain. Mining power concentration may damage the trust and secure level of blockchain systems. However, no tools or studies could timely and quantitatively tell us how decentralized in terms of mining power the PoW blockchain system is. in this article, we design a decentralization index, called Endex, to efficiently quantify the degree of mining power decentralization and timely capture the abnormal phenomena of PoW blockchain systems. We then validate the effectiveness and accuracy of Endex over real mining datasets of four popular blockchain systems. The empirical study demonstrates that Endex can well capture the changes in mining power for these blockchain systems and detect in time the abnormal incidents such as double spending attack and hard fork. [ABSTRACT FROM AUTHOR]

Optimal resource provisioning is essential for scalable big data analytics. However, it has been difficult to accurately forecast the resource requirements before the actual deployment of these applications as their resource requirements are heavily application and data dependent. This paper identifies the existence of effective memory resource requirements for most of the big data analytic applications running inside JVMs in distributed Spark environments. Provisioning memory less than the effective memory requirement may result in rapid deterioration of the application execution in terms of its total execution time. A machine learning-based prediction model is proposed in this paper to forecast the effective memory requirement of an application given its service level agreement. This model captures the memory consumption behavior of big data applications and the dynamics of memory utilization in a distributed cluster environment. With an accurate prediction of the effective memory requirement, it is shown that up to 60 percent savings of the memory resource is feasible if an execution time penalty of 10 percent is acceptable. The accuracy of the model is evaluated on a physical Spark cluster with 128 cores and 1TB of total memory. The experiment results show that the proposed solution can predict the minimum required memory size for given acceptable delays with high accuracy, even if the behavior of target applications is unknown during the training of the model. [ABSTRACT FROM AUTHOR]

Recent research trends exhibit a growing imbalance between the demands of tenants’ software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system . The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services. [ABSTRACT FROM AUTHOR]

Downlink inter-cell interference is a major limiting factor in current cellular systems. Several trends exacerbate the interference problem: cells become denser, more heterogeneous, and are more randomly deployed. To tackle this problem, there are many pure network-side solutions proposed, e.g., eICIC and feICIC in 3GPP LTE. However, these solutions are less spectrally efficient because the interferer usually needs to mute or reduce power to keep its interference power to victims below a tolerable level. In this paper, we consider an alternative solution in which the user equipment may perform interference cancellation (IC). In theory, this is a more spectrally efficient scheme as compared with the pure network-side solutions. In practice, however, to fully exploit the benefit of IC, the network should adopt some IC-aware scheduling rules. We propose a multi-cell joint scheduling, joint rate selection and power allocation problem in an IC-enabled network, trying to maximize the network utility. To make our solutions practical and readily deployable, we consider different settings. We explore both theoretically optimal IC scheme and the more practical symbol-level IC. On the other hand, we propose both dynamic and semi-static solutions, and compare them. Our simulation results show that while the average throughput increases by only around 10%, cell edge users can gain more than 50%. [ABSTRACT FROM PUBLISHER]

With the recent emergence of naked-eye 3D mobile devices and various 3D-enabled laptops, service providers now afford the opportunity to provide mobile 3D video streaming in LTE-Advanced networks. Differing from traditional single-view 3D videos, multi-view 3D videos allow users to choose preferred view angles and thus are promising for new applications, such as free-viewpoint television (FTV). Nevertheless, enabling multi-view 3D video services may overwhelm the network resource when transmitting all views of every video. Fortunately, Depth-Image-Based Rendering (DIBR) allows each mobile client to synthesize the desired view from a nearby left view and right view, so that not all views of a video are necessarily transmitted. A new challenge with DIBR, however, is to carefully choose the transmitted views to limit the video distortion and minimize the bandwidth consumption. In this paper, therefore, we first formulate a new optimization problem, called View and MCS Selection (VMS) Problem, to minimize the bandwidth consumption for multi-view 3D video multicast in LTE networks. An algorithm, called View and MCS Aggregation (VMAG) is proposed to find the optimal solution to VMS. For Carrier Aggregation (CA) in LTE-Advanced networks, we formulate a new View, MCS and Carrier Selection (VMCS) Problem and prove that the problem is NP-Hard. We first design a dynamic programming algorithm, called the View Assignment with MCS and Carrier (VAMC) algorithm, to find the optimal solution for small instances. We then propose the View and MCS Aggregation with Carrier (VMAGC) algorithm based on VMAG to effectively find the near-optimal solution to VMCS. The simulation results show that bandwidth consumption can be effectively reduced by over 30 percent in VMS and VMCS. [ABSTRACT FROM PUBLISHER]

The multichannel rendezvous problem in cognitive radio networks (CRNs) has been a hot research topic lately. One of the most challenging settings of the multichannel rendezvous problem is the oblivious rendezvous problem in heterogeneous CRNs, where: 1) there are no distinguishable roles of users; 2) users’ clocks are not synchronized; 3) users may have different available channel sets; and 4) there is no universal labelling of the channels. Most existing works in the literature focus on achieving deterministic bounds for the maximum conditional time-to-rendezvous (MCTTR) and perform poorly (in comparison with the random algorithm) for the expected time-to-rendezvous (ETTR) due to the “stay” modes in these works. In this paper, we tackle the oblivious rendezvous problem by taking both MCTTR and ETTR into consideration. In order to have guaranteed rendezvous, we only make two assumptions: (A1) there is at least one common available channel and (A2) there is a unique ID for each user. We first propose a new class of strong symmetrization mappings to encode user IDs for speeding up the rendezvous process. Two efficient and yet simple encoding schemes are proposed by utilizing the $\cal C$ -transform and the existing 4B5B encoding. Based on the new class of strong symmetrization mappings, we propose the two-prime modular clock algorithm for the two-user rendezvous problem. The ETTR of our algorithm is almost the same as that of the random algorithm and its MCTTR is also comparable to the best existing bound. We also extend the two-prime modular clock algorithm for multiuser rendezvous by proposing the stick together algorithm and the spread out algorithm. One interesting finding for the multiuser rendezvous problem is that the spread out algorithm is not always better than the stick together algorithm as commonly claimed in the literature. [ABSTRACT FROM PUBLISHER]

Ultra-dense network is a promising technology for 5G cellular networks, and its approach is to densify the network to enhance system throughput via universal frequency reuse. Traditionally, macro cells and small cells usually operate on separate dedicated channels in the ultra-dense environment. This dedicated channel approach leads to resource underutilization because base stations may not be able to fully utilize their radio resource with fixed partitioning when the traffic load in the network is fluctuating. In this paper, we study this underutilization problem in ultra-dense heterogeneous networks, and propose two adaptive channel partitioning schemes to enhance the system throughput: D2CP and D2CP-C. Both schemes require the coordination between macrocell and small cells, and D2CP-C takes advantage of cross-tier cooperation to further enhance the system throughput. We analyze the proposed schemes based on stochastic geometry, and evaluate the performance via simulations. The simulation results show that D2CP and D2CP-C outperform fixed channel partitioning by 33.37% and 76.01%, respectively, demonstrating that base station coordination and cross-tier cooperation can achieve better radio resource utilization in ultra-dense heterogeneous networks. [ABSTRACT FROM AUTHOR]

A mobile device with embedded cognitive radios (CRs) is integrated into a novel low-cost massive antenna configuration multihop or device-to-device (D2D) wireless network that temporarily uses any portion of the spectrum for transmissions. However, the number of spectral bands (SBs) that a CR can sense within a specific time is limited. Furthermore, CR node transmissions interfere with one another. This paper proposes a method both for cooperative sensing and transmission with preallocated disjoining of spectrum sections (SSs) from neighboring links to increase the likelihood of matching available SBs. This minimizes sensing and negotiating overheads and reduces interference from a viewpoint of high-level graph model and mathematical formulation. The multiple-constraint routing with the resource allocation for cooperative sensing problem is proven to be NP-hard. The resource allocation method is compared with existing SB postallocation methods (i.e., random SB allocation and fixed SB allocation) and preallocation methods (i.e., link-based SS allocation and multiple parallel SB allocation). The proposed methods handle the resource preallocation mechanism with a centralized solution but handle the sensed results with a decentralized solution to reduce the negotiating and selecting time. We evaluate the performance of the system capacity, end-to-end transmission delay, fairness index, and standard deviation, thereby verifying the quality of the proposed method in terms of interferences, the fading effect, and other environment-dependent conditions. The results of the proposed method are more favorable than those of other methods. [ABSTRACT FROM PUBLISHER]

Opportunistic routing (OR) is a promising technology for improving the throughput of wireless networks. In contrast to traditional routing techniques that forward messages along predetermined paths, OR schemes allow selection of a set of candidate nodes as potential next-hop forwarders to utilize the broadcast nature of the wireless medium. An analysis of transmission latency and signaling overhead depend on the number of hops required to transmit a packet from source to destination. In this paper, we propose three models to analyze the number of transmissions for successfully delivering a packet with three OR schemes: single-forwarder OR with known topologies, multiple-forwarder OR with known topologies, and multiple forwarder OR with unknown topologies. These models are useful in the network planning. The simulation results verified the accuracy and usability of the three models. [ABSTRACT FROM AUTHOR]

Switching off base stations (BSs) is an effective and efficient energy-saving solution for green cellular networks. The previous works focus mainly on when to switch off BSs without sacrificing the traffic demands of current active users, and then enlarge the coverage of the stay-on cells to cover as many users as possible. Based on this objective, both constant power and transmission power of each BS become the major energy consumption sources. However, the transmission powers of enlarged cells, which have not been taken into account in previous research, are not negligible as compared to other energy consumption sources. To tackle this problem, we observe that the transmission power of one specific BS could be reduced via cooperation among two or more BSs, which is typically used to improve the throughput or enhance the spectrum efficiency in wireless systems. The challenges come mainly from how to jointly consider which BSs to switch off and how to cooperate among active-mode BSs. In this paper, we design energy-aware cooperation strategies that ensure that our system is energy-saving while satisfying user demands. To cope with sleep-mode BSs and perform cooperation among active BSs, we formulate this problem as a binary integer programming problem, and prove it is NP-hard. Based on our formulation, we derive a performance lower bound for this problem via Lagrangian Relaxation with search enumeration. Furthermore, we propose two heuristic algorithms accounting for the properties of energy savings and the constraints of bandwidth resources. The simulation results show that our algorithms outperform pure power control mechanisms that do not consider the transmission power and pure cooperation without power control in terms of the total consumed energy. We also observe that larger cooperative size does not imply a better strategy under different scenarios. Compared to the total consumed energy given that all BSs are turned on, our algorithms can save up to 60 percent of energy. This demonstrates that our methods are indeed efficient energy-saving cooperation strategies for green cellular networks. [ABSTRACT FROM AUTHOR]

Concerns with global warming have prompted much research effort in energy-related issues for cellular systems. Heterogeneous networks (HetNets) incorporated with small cells save energy effectively due to the lower transmit and operational power consumptions of small cells. Multicell cooperation employing both cooperative transmission to tackle inter-cell interference and sleep mode operation to attain network energy conservation plays a decisive role in future green HetNets. Energy harvesting is another trend to achieve network energy savings. In this paper, we investigate how green multicell cooperation (GMC) can be achieved in HetNets facilitated with hybrid energy sources. Due to the lack of efficient solution approaches in coping with the spatial and temporal characteristics of the GMC problem simultaneously, we propose a greedy decomposition to solve the GMC problem via two sub-problems. We prove that the achieved grid energy consumption can be upper bounded, and observe that the tightness of the upper bounds hinges on two clustering-related factors, which may guide the scheduling design in future green HetNets. Our simulation results show that the proposed solution outperforms the existing approaches in terms of grid energy savings, and also demonstrate the cluster formation results and the dynamics of the clustering-related factors. [ABSTRACT FROM PUBLISHER]

A slow-paced persistent attack, such as slow worm or bot, can bewilder the detection system by slowing down their attack. Detecting such attacks based on traditional anomaly detection techniques may yield high false alarm rates. In this paper, we frame our problem as detecting slow-paced persistent attacks from a time series obtained from network trace. We focus on time series spectrum analysis to identify peculiar spectral patterns that may represent the occurrence of a persistent activity in the time domain. We propose a method to adaptively detect slow-paced persistent attacks in a time series and evaluate the proposed method by conducting experiments using both synthesized traffic and real-world traffic. The results show that the proposed method is capable of detecting slow-paced persistent attacks even in a noisy environment mixed with legitimate traffic. [ABSTRACT FROM PUBLISHER]

Energy efficiency is an important focus area of 5G mobile technology. Due to global warming, it is important to lower the global CO2 footprint, even with the expectation of massively increasing cellular data traffic. The ability of HetNets to support high capacity makes it a significant driver for the success of 4G and indispensable in 5G. Small cells equipped with cognitive radio (CR) techniques could improve spectrum utilization by opportunistic spectrum access and intelligent interference mitigation. In this article, we propose an RF-CoHetNet, a novel heterogeneous cellular network system where small cells possess CR capabilities and are wirelessly powered by electromagnetic RF-energy. Under this system architecture, both energy efficiency and spectral efficiency can be achieved by properly switching small cells to harvesting mode or allocating channels/power to serve their users. We show that RF-energy harvesting can provide a promising solution to further improve the energy efficiency of the whole system. Through RF-energy harvesting, the unavoidable interference turns out to be a useful energy resource to support partial power consumption in the small cell. We further propose a heuristic algorithm to minimize the total system energy consumption under the system capacity constraint. From the numerical results, we give some insights into the possibility of RF-CoHetNet deployment. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the two-user multichannel rendezvous problem in a cognitive radio network (CRN) and derive tight lower bounds for maximum time-to-rendezvous (MTTR) and maximum conditional time-to-rendezvous (MCTTR) of various channel hopping (CH) schemes under a channel loading constraint. In the symmetric and synchronous setting, we propose a novel Cycle-Adjustable Channel Hopping (CACH) scheme to achieve the MTTR lower bound (when the channel loading is bounded above by 1/u with u being a prime power). Thus, the MTTR lower bound is tight and the CACH scheme is optimal in minimizing MTTR among all the symmetric and synchronous CH schemes under the same channel loading constraint. In the asymmetric setting, we show that the classical wait-for-mommy strategy can be used to achieve the MCTTR lower bound, and thus it is optimal. In the symmetric and asynchronous setting, we also show a hierarchical construction of an asynchronous CH sequence by using two smaller asynchronous CH sequences. To further understand the effect of channel loading to the other performance metrics in a CRN, we perform various computer simulations for various CH schemes. Our simulation results show that the average time-to-rendezvous of CACH is independent of the total number of channels, and it is also robust to the disturbance of primary users. [ABSTRACT FROM PUBLISHER]

Network virtualization is an essential technique for data center operators to provide traffic isolation, differentiated service, and security enforcement for multi-tenant services. However, traditional protocols used in local area networks may not be applicable for data center networks due to the difference in network topology. Recent research suggests that layer-2-in-layer-3 tunneling protocols may be the solution to address the challenges. In this article, we find via testbed experiments that directly applying these tunneling protocols toward network virtualization only results in poor performance due to the scalability problems. Specifically, we observe that the bottlenecks actually reside inside the servers. We then propose a CPU offloading mechanism that exploits a packet steering function to balance packet processing among available CPU threads, thus greatly improving network performance. Compared to a virtualized network created based on VXLAN, our scheme improves the bandwidth for up to almost 300 percent on a 10 Gb/s link between a pair of tunnel endpoints. [ABSTRACT FROM AUTHOR]

Depth-Image-Based Rendering (DIBR) is a popular technology and enables the viewers to watch multi-view 3D videos via TV or smart phone. In this paper, we propose a game-theoretical framework over a multi-view and multi-video network where one server delivers multiple multi-view 3D videos to multiple viewers. In a mobile network, any individual viewers can subscribe to a video with any viewing angle within a specified viewing range in 3D-enabled mobile handsets. To facilitate efficient resource allocation for multi-view 3D videos, we formulate a new resource allocation problem, named Price-based 3D Viewing Range Control problem (PVRC), as a Stackelberg game in Game Theory, in which the server and the viewers are modeled as the leader and the followers, respectively, to jointly consider the revenue of the server and the utilities of the viewers. We analyze the price setting problem by investigating the Stackelberg equilibrium between the server and viewers. Simulation results demonstrate that compared to popular uniform-price setting, non-uniform pricing is an effective approach for reducing the difference of utilities among the viewers, effectively controlling the viewing ranges and avoiding starvation of the network resources to only few viewers. Moreover, we also derive the social optimum in the problem, and the simulation results show that the derived Stackelberg equilibrium is close to the social optimum. [ABSTRACT FROM AUTHOR]

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. An effective way for energy conservation of BSs is to switch BSs on/off according to the traffic profile. However, such operations may create coverage holes in the network. In this paper, we aim to minimize the total power consumption of the network by switching BSs on/off adaptively while maintaining the network coverage. We find that the BS activation problem for minimal network power consumption with full network coverage preservation is an NP-hard problem. To address the problem, we first derive the optimal cell size for minimizing BS power consumption per unit coverage area and propose a polynomial-time algorithm for energy-efficient BS activation. The simulation results show that our algorithm can approach the minimum network power consumption and adapt to network traffic load under non-uniform traffic load distributions. More importantly, we demonstrate that network densification with small cells for bursting throughput in hot spot areas can also be beneficial in saving network energy during the low traffic load period. [ABSTRACT FROM AUTHOR]

Cooperative multicasting is a promising technique to combat severe fading and path loss for wireless video delivery. In typical wireless video multicasting, nodes under the worst channel condition determine the video delivery quality, thereby limiting the system performance and affecting the Quality of Experience (QoE) of the users. Existing cooperative multicast schemes may not fully exploit the characteristics of scalable video over wireless networks. To tackle this problem, we propose two new cooperative multicast schemes called OppCM and CodedCM for scalable video delivery. OppCM performs opportunistic listening and conditional demodulating to enhance the system performance; CodedCM delivers layer-encoded video with multi-resolution modulation through cooperative multicasting. We analyze the system performance in terms of average outage probability for four cooperative multicast schemes, including direct multicast (for baseline comparison), pure cooperative multicasting, and the two proposed schemes OppCM and CodedCM. We also evaluate their performance via simulations. The results show that the proposed OppCM and CodedCM outperform the other two schemes, demonstrating that they are indeed promising solutions to wireless scalable video multicasting. [ABSTRACT FROM AUTHOR]

Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance. [ABSTRACT FROM PUBLISHER]

In this paper, we study the Roadside Unit (RSU) placement problem in vehicular networks. We focus on the highway-like scenario in which there may be multiple lanes with exits or intersections along the road. In our model, each vehicle can access RSUs in two ways: 1) direct delivery, which occurs when the vehicle is in the transmission range of the RSUs, and 2) multi-hop relaying, which takes place when the vehicle is out of RSU transmission range. We account for both access patterns in our placement strategy and formulate this placement problem via an integer linear programming model such that the aggregate throughput in the network can be maximized. We also take into account the impact of wireless interference, vehicle population distribution, and vehicle speeds in the formulation. The performance of the proposed placement strategy is evaluated via ns-2 simulations together with VanetMobisim to generate vehicle mobility patterns. The results show that our strategy leads to the best performance as compared with the uniformly distributed placement and the hot spot placement. More importantly, our solution needs the least number of RSUs to achieve the maximal aggregate throughput in the network, indicating that our scheme is indeed a cost effective yet highly efficient placement strategy for vehicular networks. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial to the implementation of broadcast and multicast services in wireless ad hoc and sensor networks. This paper proposes a path-based power-efficient approach that minimizes energy consumption in the network by optimizing the transmission ranges of all nodes. Unlike existing link-based and node-based solutions, the proposed approach, which is applicable to both static and mobile wireless networks, constructs minimum-power broadcast trees. This study uses Lagrangian Relaxation (LR) to decompose the tree-construction problem, which is NP-complete, into multiple independently solvable sub-problems. The resulting Lagrange dual solution ensures a lower bound on the objective function value. The proposed approximation heuristic solves the primal problem by obtaining the upper bound of the objective value based on the information from the set of Lagrange multipliers. This study develops a decentralized, near-optimal algorithm to solve the problem. Simulation results show that in randomly generated networks, the proposed method outperforms the existing Minimum Shortest Path Tree (MSPT) algorithm, Prim's Minimum Spanning Tree (PMST) algorithm, and the Broadcast Incremental Power (BIP) algorithm by 30, 10, and 5%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we study the issues of medium access control (MAC) for multihop wireless networks with directional antennas. Existing solutions to directional antenna MAC problems rarely account for the impact of minor lobes and typically assume that the neighboring nodes' locations are known a priori. As a result, they are not applicable to practical systems or mobile nodes. In this paper, we propose a reservation-based directional medium access control (RDMAC) protocol for multihop wireless networks based on the IEEE 802.11 distributed coordination function. The design objective of RDMAC is to increase the network throughput by reducing interference among neighboring nodes. We then develop an analytical model for RDMAC to investigate the throughput performance of RDMAC under arbitrary node density. The analytical model provides insights into RDMAC parameter settings. We also evaluate the performance of the proposed RDMAC through ns-2 simulations. The results validate the accuracy of our analytical model and show that our mechanism outperforms existing solutions, in terms of higher throughput. [ABSTRACT FROM PUBLISHER]