Sangkil Jung

Effect of RObust Header Compression (ROHC) and Packet Aggregation on Multi-hop Wireless Mesh Networks

This paper evaluates the behavioral effect of RObust Header Compression (ROHC) and packet aggregation in multi-hop wireless mesh networks. ROHC itself shows around 20% achieved rate improvement on a line of wireless link even if as the number of mesh router increases, total achieved rate gradually decreases. Packet aggregation cooperating with ROHC provides 4 to 10 times achieved rate improvement and results in maximum 6 times reduction of end-to-end delay. Even if the ROHC and packet aggregation gives such a promising improvement, we need to identify effect of the processing time of the two schemes on overall wireless mesh network behaviors. To do this work, we measure/use ROHC processing time from Pentium 4 and RouterBOARD 230, and apply the results into NS-2 simulations. The simulation results show high performance improvement from ROHC and packet aggregation deteriorates as a general purpose processor in a mesh router takes much time to deal with the two schemes. This result motivates a hardware design be required to speed up the processing units of the two schemes. Consequently, we propose a hardware system model for ROHC and packet aggregation by using SystemC Hardware Description Language (HDL). From the SystemC results, we conclude the proposed built-in processor design for the two schemes keeps performance improvement by enhancing the low speed processing power of general purpose processors.

Voice Transmission Enhancing Model on Wireless Mesh Networks

This paper initially shows ROHC and packet aggregation significantly improve the number of successful voice calls. However, the improvement does not include processors processing overhead, which is identified by measuring ROHC processing time from Intel Pentium 4 and RouterBOARD, and applying the results into NS-2 simulations. Simulation results indicate the number of successful voice calls is seriously affected by the processing overhead. For the solution of the processing limitation, we propose a hardware model of the two algorithms and numerically analyze the model. Simulation results show the hardware model makes the number of successful voice calls approach the ideal (no-delay) case, and the numerical model exactly characterizes the hardware behaviors.

Performance analysis of queue scheduling mechanisms for EF PHB and AF PHB in DiffServ networks

Expedited Forwarding Per-Hop Behavior (EF PHB) and Assured Forwarding Per-Hop Behavior (AF PHB) are the forwarding schemes operating in DiffServ networks. EF PHB is appropriate for flows with low delay, low latency and low jitter, and AF PHB provides guaranteed services with minimum bandwidth and buffer. The two PHB can be implemented by several queue scheduling mechanisms such as weighted round-robin (WRR), priority round-robin (PRR) and priority-weight round-robin (PWRR) which is the queue scheduling mechanism conjoining the WRR and PRR. The EF queue in PWRR is served based on PRR, and WRR mechanism is used in scheduling non-EF queues. In this paper, we evaluate the WRR, PRR and PWRR under the conditions that both EF PHB and AF PHB operate in a DiffServ router. We show PWRR is the best queue scheduling mechanism simultaneously gratifying the delay and jitter criteria of the EF class as well as the target throughput of AF classes.

Network/hardware cross-layer evaluation for ROHC and packet aggregation on wireless mesh networks

This paper proposes a profile-based network and hardware co-simulation method to investigate the overall performance and real-timing characteristics of a wireless mesh network (WMN) affected by hardware capabilities, speed and complexity. For the sophisticated algorithms to be assisted by a hardware realization, we adopt the RObust Header Compression (ROHC) and packet aggregation that provide high and reliable data transmission over unstable wireless links, which is proven in the preliminary works. To verify the hardware support needs to get the benefit of the two algorithms, we measure the ROHC processing time from Intel Pentium 4 and RouterBOARD, and identify the deterioration of sensor throughput and successful voice calls under various NS-2 simulation scenarios. The co-simulation method integrates the network level simulator, NS-2 and hardware level simulator, SystemC. In this approach, we first insert the modules of ROHC and packet aggregation algorithms into the network simulator hierarchy, and measure the packet arrival times. Then, the corresponding hardware architecture is designed by SystemC for profiling the hardware delay appeared in encoding and decoding packets. The hardware is suitably designed to reduce the complexity and make a sufficient speedup in the packet processing. Finally, the traced hardware delays are applied into the network level simulator to extract real-timing WMN behaviors changed by the hardware operations in each mesh router.

On Achieving High Performance Wireless Mesh Networks With Data Fusion

Wireless Mesh Network (WMN) supports various types of mesh clients such as sensor networks, cellular; 802.11/e Wireless LAN, etc. In this case, it needs to be suitably organized and adopt well-known or newly proposed algorithm to guarantee high and reliable mesh performance. This paper applies RObust Header Compression (ROHC) originate from cellular data network plus packet aggregation to WMNs when mesh clients generate small-size fusion data, i.e., sensor (delivered by UDP) and voice over IP (VOIP) data. We preliminarily analyze ROHC and packet aggregation improve UDP throughput and the number of supported voice calls. However; the improvement does not include a processors processing overhead for ROHC and packet aggregation, which is examined by measuring ROHC execution time from Intel Pentium 4 and RouterBOARD, and applying the results into NS-2 simulations. The results indicate UDP and voice performance are seriously affected by the processing overhead. As a solution for the performance degradation, we introduce a hardware design for the two algorithms. By using SystemC Hardware Description Language (HDL), we first design hardware specification composed of 1Ghz master clock and 333Mhz BUS system. Then, we integrate the hardware model into previous NS-2 network model, and propose profile-based network/hardware co-simulation method which gives insight how to investigate real-timing characteristics of the hardware model in the view of network behaviors.

Data traffic analysis in wireless fusion network with multiple sensors

In this paper, we consider a wireless fusion sensor network where multiple types of sensors are incorporated in each sensor node. Each sensor node incorporates a RFID reader and a acoustic sensor. The RFID reader senses the proximity of target objects and acoustic sensors detect the objects for position estimations. The object positions estimated by both sensors are compensated by visual sensor to obtain more accurate object coordinates. In this paper, we perform network traffic analysis for the wireless fusion sensor network by using NS-2 simulations under various network scenarios. The analysis includes the feasibility of fusion network, hop-by-hop delay, and end-to-end delay.

On Addressing Network Synchronization in Object Tracking with Multi-modal Sensors

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

Numerical analysis of hardware architecture for header compression and packet aggregation on wireless networks

This paper proposes a numerical analysis model to predict the processing delay of a hardware architecture for robust header compression and packet aggregation on wireless mesh networks. The analysis model is composed of a series of queue systems such as G/M/1, M[K]/M/1, M/M/1, and M/M/∞ that are one-to-one mapped into the constructed hardware components to characterize the concurrent operations and interactional relationship between encoding and decoding paths. Based on the co-simulation method which integrates NS-2 and SystemC, we show the analysis model properly approximates the processing delay of the hardware architecture. Additionally, the variation of processing delay occurring when a part of hardware components are differently configured is suitably characterized by the proposed model, and the overall mesh network behaviors is predicted by applying the numerical results into NS-2 simulations.

Statistical Estimation and Adaptation for Visual Compensation in Object Tracking

The multi-modal tracking model in [1] enables the on-the-fly error compensation with low complexity by adopting acoustic sensors for the main tracking task and visual sensors for correcting possible tracking errors. The visual compensation process in the model is indispensable to the accurate tracking task in a dynamic object movement. This article proposes an algorithm to approximate the successful visual compensation rate appearing in the multi-modal tracking system. The acoustic sampling interval of the object signal and the random occurrence of transmission delays of multi-modal data are critical to the compensation process. Therefore, by using the two key factors as parameters, the algorithm called SEA can estimate the successful visual compensation of a tracking system. After we build up a tracking system, it is required to maintain the system at a certain level of tracking accuracy. This task can be done by controlling the aforementioned parameters since the visual compensation influences the tracking accuracy. Thus, we propose another algorithm called SEA2 for the parameter adaptation. The algorithm controls only acoustic sampling interval due to the easiness of adjustment and having the main impact on the success of the visual compensation. From the algorithm validation, we show the SEA properly quantifies the visual compensation process successfully occurring in the tracking scenarios, and SEA2 is feasible for parameter adaptation and achieving the target level of accuracy.

Guarantee of service expectation level and fairness by precise queue weight allocation in AF PHB

Assured forwarding per-hop behavior (AF PHB) is a forwarding scheme for providing QoS in DiffServ networks. It defines four classes and three drop precedence, where buffer space and bandwidth are defined as forwarding resources for each AF class (J. Heinanen et al., 1999). RIO (D.D. Clark and W. Fang, 1998) could be used as drop policy in a DiffServ router. AF PHB could be implemented by WRR, WFQ, CBQ, etc. When network managers provide AF PHB using the scheduling mechanisms, they want to guarantee accurate achievement of service expectation level, which results from precise queue weight allocation to each AF class with respect to forwarding resources, i.e., buffer space and bandwidth. In order to provide a guideline for the precise queue weight allocation, this paper proposes a simple analytical model for AF PHB designed on RIO and M/M/1 queue, and uses this model to obtain queue weight equation (QWE), which performs fine calculation of queue weight for AF classes. Average buffer space and bandwidth allocated for each AF class are used in derivation of QWE. The model verification is performed by simulation in which we use WRR for simplicity. Simulation results show that the QWE supports an accurate allocation of the forwarding resources, and, as a result, makes four AF classes achieve fairness as well as service expectation levels