Bongjun Ko

Distributed Channel Assignment in Multi-Radio 802.11 Mesh Networks

To increase the utilization of the available frequency channel space in 802.11-based wireless mesh networks, recent work has explored solutions based on multi-radio stations. This paper reports on our design and experimental study of a distributed, self-stabilizing mechanism that assigns channels to multi-radio nodes in wireless mesh networks. We take a modular approach by decoupling the channel selection decision from the data forwarding mechanism, which makes our solution readily applicable to real-world operation when used with emerging multi-radio routing solutions. We demonstrate the efficacy of our protocol on a real-world, 14-node testbed comprised of nodes, each equipped with an 802.11a card and an 802.11g card. We show via extensive measurements on our testbed that our channel assignment algorithm improves the network capacity by 50% in comparison to a homogeneous channel assignment and by 20% in comparison to a random assignment.

An information-centric architecture for data center networks

We propose a new Data Center Network (DCN) architecture, based on the principles of Information-Centric Networking (ICN). Our Info-Centric Data Center Network (IC-DCN) addresses many of the pain-points in current DCNs, such as network scalability, host mobility, etc. At the same time, IC-DCN introduces a number of new features to the current information-centric network architectures. We achieve this goal by decoupling the control-plane and data-plane functionalities. The control-plane is implemented in a centralized manner, while the data-plane is fully distributed. We show that IC-DCN effectively addresses many of the ICN challenges in the data center, such as routing scalability, full network utilization, and name space management.

Cooperative transmit-power estimation under wireless fading

We study blind estimation of transmission power of a node based on received power measurements obtained under wireless fading. Specifically, the setup consists of a set of monitors that measure the signal power received from the transmitter, and the goal is to utilize these measurements to estimate the transmission power in the absence of any prior knowledge of the transmitters location or any statistical distribution of its power. Towards this end, we exploit spatial diversity in received-power measurements and cooperation among the multiple monitoring nodes; based on theoretical analysis we obtain the Maximum Likelihood (ML) estimate, derive fundamental geometrical insights and show that this estimate is asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimators through simulations and on a data-set of field measurements.

A circulatory system approach for wireless sensor networks

One of the challenges in a military wireless sensor network is the determination of an information collection infrastructure which minimizes battery power consumption. The problem of determining the right information collection infrastructure can be viewed as a variation of the network design problem, with the additional constraints related to battery power minimization and redundancy. The problem in its generality is NP-hard and various heuristics have been developed over time to address various issues associated with it. In this paper, we propose a heuristic based on the mammalian circulatory system, which results in a better solution to the design problem than the state of the art alternatives.

Dynamic spectrum allocation under cognitive cell network for M2M applications

In machine-to-machine (M2M) communication, smart spectrum management is vital for the system performance since there are a large number of wireless devices sharing the same limited spectrum and the spectrum resource is scarce. For the spectrum sharing, its not only encountered in ISM band, but also in the networks with dedicated spectrums when the network is under evolution or upgrading, such as the system updating from narrow-band to broadband in SmartGrid communications. In this paper, we summarize the typical methods in wireless cellular network to improve the spectrum utilization and compare their advantages and limitations as applied to M2M communications. Based on the analysis, we propose a novel mechanism for dynamic spectrum allocation in a cognitive radio environment for SmartGrid applications using OFDMA technology and give some evaluations based on field test data. The spectrum allocation we proposed can be divided into two stages: network entry stage based on initial sensing and dynamic interference avoidance stage based on periodical sensing. At the network entry stage, we focus the discussion on the design of optimal spectrum auto planning algorithm and provide a new scheme based on backup list generation and internal/external interference differentiation. After the OFDMA based system enters into normal communication stage, interference avoidance within the allocated spectrum band is the main task of the M2M system. The in-band spectrum will be segmented into several sub-bands and be scheduled by the system based on periodical spectrum sensing results to avoid interferences coming from external devices(SCADA or unknown system) which share the same spectrum with the current system. The spectrum allocation mechanism we proposed has been used in IBM Wireless Internet-of-Thing (IoT) platform and achieved good performance during the field test.

netCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in Computer Networks

We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose pruning and thresholding along with a dynamic threshold value selector, to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies.

Dynamic placement of composite software services in hybrid wireless networks

The dynamic environment of hybrid mobile and fixed wireless networks used in military operations poses significant challenges in the efficient provisioning of software functionality to application clients. With their transient topology, the software services hosted on mobile nodes may become temporarily unavailable or the cost of transferring data across the network may become too high. To address this problem we have designed a placement technique that allows the dynamic repositioning of services within the network as it evolves. The technique repositions services in reaction to changes in network topology as well as in various system properties, such as service dependencies or the workload generated by application clients. In our approach we use a multi-layer model to represent a service-based software system embedded in a network topology. We apply constraint programming and its linear programming relaxation to solve the model to find where best to place or reposition the services. We evaluate the technique in terms of its effectiveness and cost under various simulated operational conditions.

Understanding the Quality of Monitoring for Network Management

The vitality and utility of a network are affected significantly by the network management system (NMS) that is used to administer and monitor the network. However, models that can characterize the quality of a NMS are generally missing in the literature. In this paper, we introduce the concept of quality of monitoring (QoM), provide a mathematical formulation based on stochastic processes that can be used to model a network monitoring system and define QoM metrics based on this formulation. A formal analysis of the proposed framework along various metrics is also provided, along with a case study of its application to network monitoring in a mobile ad hoc network.

Inferring Smartphone Users' Handwritten Patterns by using Motion Sensors.

Mobile devices including smartphones and wearable devices are increasingly gaining popularity as platforms for collecting and sharing sensor data, such as the accelerometer, gyroscope, and rotation sensor. These sensors are used to improve the convenience of smartphone users, e.g., supporting the mobile UI motionbased commands. Although these motion sensors do not require users’ permissions, they still bring potential risks of leaking users’ private information reflected by the changes of sensor readings. In this paper, we investigate the feasibility of inferring a user’s handwritten pattern on a smartphone touchscreen by using the embedded motion sensors. Specifically, our inference attack is composed of two key steps where we 1) first exploit the dynamic time warping (DTW) technique to differentiate any pair of time-series sensor recordings corresponding to different handwritten patterns; and 2) develop a novel sensor fusion mechanism to integrate information contained in multiple motion sensors by exploiting the majority voting strategy. Through extensive experiments using real-world data sets, we demonstrate the effectiveness of our proposed attack which can achieve 91.4% accuracy for inferring smartphone users’ handwritten patterns.

Balancing distributed analytics' energy consumption using physics-inspired models

With the rise of small, networked sensors, the volume of data generated increasingly require curation by AI to analyze which events are of sufficient importance to report to human operators. We consider the ultimate limit of edge computing, when it is impractical to employ external resources for the curation, but individual devices have insufficient computing resources to perform the analytics themselves. In a previous paper we introduced a decenralized method that distributes the analytics over the network of devices, employing simulated annealing, based on physics-inspired Metropolis Monte Carlo. If the present paper we discuss the capability of this method to balance the energy consumption of the placement on a network of heterogeneous resources. We introduce the balanced utilization index (BUI), an adaptation of Jain’s Fairness Index, to measure this balance.