Programmable switches have been touted as an attractive alternative for deploying network functions (NFs) such as network address translators (NATs), load balancers, and firewalls. However, their limited memory capacity has been a major stumbling block that has stymied their adoption for supporting state-intensive NFs such as cloud-scale NATs and load balancers that maintain millions of flow-table entries. In this paper, we explore a new approach that leverages DRAM on servers available in typical NFV clusters. Our new system architecture, called TEA (Table Extension Architecture), provides a virtual table abstraction that allows NFs on programmable switches to look up large virtual tables built on external DRAM. Our approach enables switch ASICs to access external DRAM purely in the data plane without involving CPUs on servers. We address key design and implementation challenges in realizing this idea. We demonstrate its feasibility and practicality with our implementation on a Tofino-based programmable switch. Our evaluation shows that NFs built with TEA can look up table entries on external DRAM with low and predictable latency (1.8-2.2 μs) and the lookup throughput can be linearly scaled with additional servers (138 million lookups per seconds with 8 servers).

Network switches are an attractive vantage point to serve various network applications and functions such as load balancing and virtual switching because of their in-network location and high packet processing rate. Recent advances in programmable switch ASICs open more opportunities for offloading various functionality to switches. However, the limited memory capacity on switches has been a major challenge that such applications struggle to deal with. In this paper, we envision that by enabling network switches to access remote memory purely from data planes, the performance of a wide range of applications can be improved. We design three remote memory primitives, leveraging RDMA operations, and show the feasibility of accessing remote memory from switches using our prototype implementation.

We present the design and implementation of p4v, a practical tool for verifying data planes described using the P4 programming language. The design of p4v is based on classic verification techniques but adds several key innovations including a novel mechanism for incorporating assumptions about the control plane and domain-specific optimizations which are needed to scale to large programs. We present case studies showing that p4v verifies important properties and finds bugs in real-world programs. We conduct experiments to quantify the scalability of p4v on a wide range of additional examples. We show that with just a few hundred lines of control-plane annotations, p4v is able to verify critical safety properties for switch.p4, a program that implements the functionality of on a modern data center switch, in under three minutes.

Today's cloud infrastructure is often overwhelmed by inputs from multiple users and administrators for enforcing the policies on which to run cloud services, and infrastructure administrators need to configure policies on different resource types such as compute, network, etc. Such complex policy enforcement decisions from multiple users could result in errors and conflicts. To mitigate such complexities in managing cloud infrastructure, there is a strong push towards decoupling high level intents (“what” should be done) from the underlying infrastructure implementations (“how” to do it). Unlike existing solutions which resolve conflicts at low level during run-time, intent-based systems aim to resolve potential conflicts at the intent specification level. To efficiently handle large scale cloud environments, we propose a Label Management Service (LMS) which provides meaningful abstractions and their relationships by analyzing target cloud infrastructure. It helps the cloud administrators to model their policy requirements efficiently by decoupling the intents from underlying specifics. LMS scales to large dynamic cloud environments and manages the life cycle of label-based intent and enforcement.

In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously.

We present NetCache, a new key-value store architecture that leverages the power and flexibility of new-generation programmable switches to handle queries on hot items and balance the load across storage nodes. NetCache provides high aggregate throughput and low latency even under highly-skewed and rapidly-changing workloads. The core of NetCache is a packet-processing pipeline that exploits the capabilities of modern programmable switch ASICs to efficiently detect, index, cache and serve hot key-value items in the switch data plane. Additionally, our solution guarantees cache coherence with minimal overhead. We implement a NetCache prototype on Barefoot Tofino switches and commodity servers and demonstrate that a single switch can process 2+ billion queries per second for 64K items with 16-byte keys and 128-byte values, while only consuming a small portion of its hardware resources. To the best of our knowledge, this is the first time that a sophisticated application-level functionality, such as in-network caching, has been shown to run at line rate on programmable switches. Furthermore, we show that NetCache improves the throughput by 3-10x and reduces the latency of up to 40% of queries by 50%, for high-performance, in-memory key-value stores.

A Multi-User MIMO (MU-MIMO) Access Point (AP) can obtain a capacity gain by simultaneously transmitting to multiple clients. This technique requires Channel State Information (CSI) at the transmitting AP to set antenna gains and phases to enable simultaneous reception through beamforming. The AP must also select both the mode (number of transmit and collective receive antennas) and the user set prior to transmission. While the ideal mode and user selection is a function of CSI, CSI must be estimated with an overhead intensive channel sounding process. We design, implement, and evaluate Pre-sounding User and Mode selection Algorithm (PUMA), a method for mode and user selection prior to channel sounding. We show that even without CSI, PUMA (i) exploits theoretical properties of MU-MIMO system scaling with respect to mode, (ii) characterizes the relative cost of each potential mode, and (iii) estimates per-stream transmission rate and aggregate throughput in each mode for a potential user set, all without CSI. Once PUMA has selected the appropriate mode and user group, the chosen protocol's channel sounding method is used on the intended user subset to carry out the transmission. We show that, on average, PUMA selects the mode and group that achieves an aggregate rate within 3% of the saturation throughput of what would have been achieved by sounding all users (which would require significant additional overhead). Moreover, we show that PUMA obtains 30% higher aggregate throughput compared to the best fixed-mode policy that uses the maximum number of available transmit and receive antennas.

Cloud-RANs (Radio Access Networks) assume the existence of a high-capacity, low-delay/latency fronthaul to support cooperative transmission schemes such as CoMP (Coordinated Multi-Point) and coordinated beamforming. However, building such hierarchical wired fronthauls is challenging as the typical I/Q data stream is non-elastic - I/Q data over the wired fronthaul has little tolerance for delay jitters and zero tolerance for losses. Any distortion to the I/Q data stream will make the resulting wireless transmission completely unintelligible. We propose Spiro, a mechanism that efficiently transports RF signals over a wired fronthaul network. The primary goal of Spiro is to make I/Q data streams elastic and resilient to unexpected network condition changes. This is accomplished through a novel combination of compression and data prioritization of I/Q data on the wired fronthaul. For a given wireless throughput, Spiro can reduce the bandwidth demand of the fronthaul data stream by up to 50% without any noticeable degradation in the wireless reception quality. Further bandwidth reduction via compression and frame losses only have a limited impact on the wireless throughput.

We present Policy Graph Abstraction (PGA) that graphically expresses network policies and service chain requirements, just as simple as drawing whiteboard diagrams. Different users independently draw policy graphs that can constrain each other. PGA graph clearly captures user intents and invariants and thus facilitates automatic composition of overlapping policies into a coherent policy.

Software Defined Networking (SDN) and cloud automation enable a large number of diverse parties (network operators, application admins, tenants/end-users) and control programs (SDN Apps, network services) to generate network policies independently and dynamically. Yet existing policy abstractions and frameworks do not support natural expression and automatic composition of high-level policies from diverse sources. We tackle the open problem of automatic, correct and fast composition of multiple independently specified network policies. We first develop a high-level Policy Graph Abstraction (PGA) that allows network policies to be expressed simply and independently, and leverage the graph structure to detect and resolve policy conflicts efficiently. Besides supporting ACL policies, PGA also models and composes service chaining policies, i.e., the sequence of middleboxes to be traversed, by merging multiple service chain requirements into conflict-free composed chains. Our system validation using a large enterprise network policy dataset demonstrates practical composition times even for very large inputs, with only sub-millisecond runtime latencies.

WiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services.

Resource conflicts are inevitable on any shared infrastructure. In Software-Defined Networks (SDNs), different controller modules with diverse objectives may be installed on the SDN controller. Each module independently generates resource requests that may conflict with the objectives of a different module. For example, a controller module for maintaining high availability may want resource allocations that require too much core network bandwidth and thus conflict with another module that aims to minimize core bandwidth usage. In such a situation, it is imperative to identify and install resource allocations that achieve network wide global objectives that may not be known to individual modules, e.g., high availability with acceptable bandwidth usage. This problem has received only limited attention, with most prior work focused on detecting, avoiding, and resolving rule-level conflicts in the context of OpenFlow.

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3~4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.

Providing bandwidth guarantees to specific applications is becoming increasingly important as applications compete for shared cloud network resources. We present CloudMirror, a solution that provides bandwidth guarantees to cloud applications based on a new network abstraction and workload placement algorithm. An effective network abstraction should enable applications to easily and accurately specify their requirements, while simultaneously enabling the infrastructure to provision resources efficiently for deployed applications. Prior research has approached the bandwidth guarantee specification by using abstractions that resemble physical network topologies. We present a contrasting approach of deriving a network abstraction based on application communication structure, called Tenant Application Graph or TAG. CloudMirror also incorporates a new workload placement algorithm that efficiently meets bandwidth requirements specified by TAGs while factoring in high availability considerations. Extensive simulations using real application traces and datacenter topologies show that CloudMirror can handle 40% more bandwidth demand than the state of the art (e.g., the Oktopus system), while improving high availability from 20% to 70%.

Spectrum management and device coordination for dynamic spectrum access (DSA) networks have received significant research attention. However, current wireless devices have yet to fully embrace DSA networks due to the difficulties in realizing spectrum-agile communications. We address the practical hurdles and present solutions toward implementing DSA devices, answering an important question “what is a simple practical extension to current wireless devices that makes them spectrum-agile?” To this end, we propose RODIN, a general per-frame spectrum-shaping protocol that has the following features to support DSA in commercial off-the-shelf (COTS) wireless devices: direct manipulation of passband signals from COTS devices, fast FPGA-based spectrum shaping, and a novel preamble design for spectrum agreement. RODIN uses an FPGA-based spectrum shaper together with a preamble I-FOP to achieve per-frame spectrum shaping with a delay of under 10 μs.

Software-Defined Networking (SDN) promises to enable vigorous innovation, through separation of the control plane from the data plane, and to enable novel forms of network management, through a controller that uses a global view to make globally-valid decisions. The design of SDN controllers creates novel challenges; much previous work has focused on making them scalable, reliable, and efficient.

The growing demand for real-time streaming video on portable devices has increased the importance of multimedia multicast in mobile wireless networks. A defining characteristic of such multicast networks is its heterogeneity in both the channel states and the MIMO capabilities of its clients. However, current wireless multicast schemes adapt poorly to such heterogeneity. We introduce Procrustes, a multimedia multicast scheme that is built upon a novel PHY-layer rateless code. Unlike bit-level rateless codes (such as Raptor [14] codes), Procrustes clients automatically adjust the PSNR of the received multicast video stream to match both the instantaneous channel state and the number of active receive antennas. We demonstrate the performance of Procrustes in a simulated environment.

Wireless performance depends directly on the quality of the channel. A wireless transmitter can improve its performance by estimating and transmitting on only the strongest channel, which can be of significantly higher quality than a weak channel (yielding up to 100% rate improvement). It is considered impossible to predict the quality of the unseen channels. Thus, the only way to identify the strongest channel is by probing each channel individually, incurring large over- heads. The key contribution of this paper is a discovery of previously unobserved properties of the wireless channel that makes it possible to predict the the strongest of a set of channels from the measurements collected only on a single channel. We confirm the properties through measurements and present a theoretical analysis that explains their nature. Our proposed system, CSpy, utilizes these observations to predict the strongest channel. CSpy is the first to reliably estimate the strongest channel by utilizing channel responses extracted from off-the-shelf wireless chipsets, without probing any additional channels. By tracking the strongest channel, CSpy improves performance by up to 100% in comparison to channel agnostic schemes.

Despite of several years of innovative research, indoor localization is still not mainstream. Existing techniques either employ cumbersome fingerprinting, or rely upon the deployment of additional infrastructure. Towards a solution that is easier to adopt, we propose CUPID, which is free from these restrictions, yet is comparable in accuracy. While existing WiFi based solutions are highly susceptible to indoor multipath, CUPID utilizes physical layer (PHY) information to extract the signal strength and the angle of only the direct path, successfully avoiding the effect of multipath reflections. Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP). Real-world indoor experiments using off-the-shelf wireless chipsets confirm the feasibility of CUPID. In addition, while previous approaches rely on multiple APs, CUPID is able to localize a device when only a single AP is present. When a few more APs are available, CUPID can improve the median localization error to 2.7m, which is comparable to schemes that rely on expensive fingerprinting or additional infrastructure.

We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.

One of the key features of high speed WLAN such as 802.11n is the use of MIMO (Multiple Input Multiple Output) antenna technology. The MIMO channel is described with fine granularity by Channel State Information (CSI) that can be utilized in many ways to improve network performance. Many complex parameters of a MIMO system require numerous samples to obtain CSI for all possible channel configurations. As a result, measuring the complete CSI space requires excessive sampling overhead and thus degrades network performance. We propose CSI-SF (CSI Sampling & Fusion), a method for estimating CSI for every MIMO configuration by sampling a small number of frames transmitted with different settings and extrapolating data for the remaining settings. For instance, we predict CSI of multi-stream settings using CSI obtained only from single stream packets. We evaluate the effectiveness of CSI-SF in various scenarios using our 802.11n testbed and show that CSI-SF provides an accurate, complete knowledge of the MIMO channel with reduced overhead from traditional sampling. We also show that CSI-SF can be applied to network algorithms such as rate adaptation, antenna selection and association control to significantly improve their performance and efficiency.

Spectrum management and device coordination for Dynamic Spectrum Access (DSA) networks have received significant research attention. However, current wireless devices have yet to fully embrace DSA networks due to the difficulties in realizing spectrum-agile communications. We address the practical hurdles and present solutions towards implementing DSA devices, answering an important question "what is a simple practical extension to current wireless devices that makes them spectrum-agile?" To this end, we propose RODIN, a general per-frame spectrum-shaping protocol that has the following features to support DSA in commercial off-the-shelf (COTS) wireless devices: (a) direct manipulation of passband signals from COTS devices, (b) fast FPGA-based spectrum shaping, and (c) a novel preamble design for spectrum agreement. RODIN uses an FPGA-based spectrum shaper together with a preamble I-FOP to achieve per-frame spectrum shaping with a delay of under 10 μ s.

WoWMoM has been known for its successful workshop programs as well as its high-quality main conference papers. WoWMoM has identified emerging hot workshop topics which also continued to last and grow in the field. The timely selections of new topics have yielded several multi-year workshops proudly hosted by this year WoWMoM. Some previous WoWMoM workshops have grown to standalone conferences or continue to exist in other major mobile & wireless networking venues.

We explore the design of a high capacity multi-radio wireless network using commercial 802.11n hardware. We first use extensive real-life experiments to evaluate the performance of closely located 802.11n radios. We discover that even when tuned to orthogonal channels, co-located 802.11n radios interfere with each other and achieve significantly less throughput than expected. Our analysis reveals that the throughput degradation is caused by three link-layer effects: (i) triggering of carrier sensing, (ii) out of band collisions and (iii) unintended frequency adaptation. Using physical layer statistics, we observe that these effects are caused by fundamental limitations of co-located radios in achieving signal isolation. We then consider the use of beamforming antennas, shielding and antenna separation distance to achieve better signal isolation and to mitigate these problems. Our work profiles the gains of different physical isolation approaches and provides insights to network designers to realize high-performance wireless networks without requiring synchronization or protocol modifications.

We present an experimental performance evaluation study of WiFi links in an open-space outdoor environment. We consider a large scale wireless sensor network scenario of seismic data collection from sensors that are buried in ground and a set of access points (APs) form the hierarchical aggregation layer and the backbone of the network. We conduct two different link characterization studies. First, we evaluate the links between the sensor nodes and a wireless AP using IEEE 802.11a/b/g. We construct the path loss model and investigate the reachability distance of this link for different protocols and different sensor node antenna heights. We then characterize the long distance wireless backhaul links between the APs. We use 802.11n and high gain directional antenna for high throughput and long distance. We evaluate how different PHY and MAC layer enhancements of 802.11n impacts its performance in an open outdoor environment. We observed up to 148 Mb/s throughput at 800 meter line-of-sight links without sophisticated tuning of antenna orientation. We believe our findings can be a benchmark for WiFi based outdoor network deployment, especially for high throughput long distance links.

Network device vendors have recently opened up the processing capabilities on their hardware platform to support third-party applications. In this paper, we explore the requirements and overheads associated with co-locating middlebox functionality on such computing resources on networking hardware. In particular, we use an example of TCP acceleration proxy (CHART) that improves throughput over networks with delay and loss. The CHART system, developed by HP and its partners provides enhanced TCP/IP performance and service quality guarantees by deploying performance accelerating proxies, which enables legacy clients to benefit by high-performance network service. Use of the TCP proxy, however, requires manual configuration on the clients changing http proxy and/or routing table settings. Can we remove the need to configure end-hosts by inserting a transparent TCP proxy in the path, without losing performance? To address this question, we implement the accelerator on HP's x86-based processing blade designed to integrate network applications within switch architecture as well as on low-end home routers with OpenWRT. We describe the implementation detail such as flow redirection for transparency and new mechanisms required for easy insertion of proxies in the network path. We also evaluate its performance on HP's experimental testbed in terms of throughput and additional processing overhead.

In Wireless Local Area Networks (WLANs), channel management is important in achieving reliable data communications and satisfying QoS requirements. The key aspects of wireless channel management are monitoring the channel quality and adapting quickly to the network conditions by switching to a better channel. We propose a wireless channel monitoring system with co-located monitoring surrogates. Our system works on multi-radio Access Points (APs) where a co-located surrogate radio monitors the condition of various channels while the master radio serves the clients for data communication. Although we have designed our system for generic WLANs, we believe it will be most useful for IEEE 802.11n networks where there are a large number of channels and dynamic frequency selection is required. Our system enables intelligent, fast channel adaptation, reduces service disruption time, and consequently helps realize the performance potential of 802.11n. We present our multi-radio co-located wireless channel monitoring surrogate system and evaluate its effectiveness on our IEEE 802.11n network testbed. We also perform case studies to demonstrate the benefit our system brings compared against the existing schemes.

Traditionally, in-network services like firewall, proxy, cache, and transcoders have been provided by dedicated hardware middleboxes. A recent trend has been to remove the middleboxes by deploying the network services into switch/router-integrated computing modules or separate server/blade machines. In this abstract, by using a web Ad-insertion application as an example, we demonstrate our in-network processing (INP) framework that orchestrates various computing resources and network devices and enables seamless and efficient deployments of network services.

In simulating wireless networks, modeling of the physical layer behavior is an important yet difficult task. Modeling and estimating wireless interference is receiving great research attention, and is crucial in a wireless network performance study. The implementation of physical layer capture, preamble detection, and carrier sense threshold plays an important role in successful frame reception in the presence of interference. We showed in our previous testbed study that the operations of the frame reception and the capture effect in real IEEE 802.11a systems differ from those of popular research simulators. We present our modifications of the IEEE 802.11a PHY models to the current simulators. The modifications can be summarized as follows. (i) The current simulators' frame reception is based only on the received signal strength. However, the real 802.11 systems can start the frame reception only when the Signal-to-Interference Ratio (SIR) is high enough to detect the preamble. (ii) Different chipset vendors implement the frame reception and capture algorithms differently, resulting in different operations for the same event. We provide different simulation models for several popular chipset vendors and show the performance differences between the models. (iii) The current simulators set the carrier sense threshold equal to the receiver sensitivity. The standard however states that it should be 20 dB higher than the receiver sensitivity. We implement our modifications to the QualNet simulator and conduct a wireless network performance study to evaluate the impact of PHY model implementation.

In wireless networks where communications are made over a shared medium, interference and collisions are the primary causes of packet drops. In multi-hop networks such as wireless mesh networks, due to the hidden terminal problem, limiting the effects of collisions and interference is a key in achieving high performance. Typical wireless medium access control protocols perform carrier sensing to avoid collisions. Most research efforts so far has used the binary model when studying carrier sensing and interference; a node is either carrier sensed or not, and a link is either interfered or not. In reality however, there exists a gray zone. Carrier sensing and interference should be represented in continuous values. Using the measurement data from our 802.11a wireless mesh network test-bed, we propose metrics that represent the levels of carrier sensing and interference. Using our metrics, we also propose methods to estimate broadcast throughput and goodput. We evaluate the accuracy of our methods by comparing our models with the measured data. In addition, we investigate the impact of the capture effect on interference.

Recently, the radio frequency identification (RFID) technology has gained significant attention. One of the important performance issues in RFID systems is to resolve the collision among responses from RFID tags from the viewpoint of wireless media access control. We consider two kinds of smart antenna systems to enhance the RFID tag reading rate, namely the adaptive array antenna and the multiple-input multiple-output (MIMO) antenna. We consider passive tags that are operating without battery. We evaluate how much performance can be improved by employing smart antennas in the cases of the binary tree splitting algorithm and the Slotted-Aloha algorithm.