Hailing Zhu

Watermarking for JPEG image using error correction coding

Compression is the most common process that limits the robustness of watermarking. We propose a watermarking method for JPEG compressed color images in the semi-decompression domain, using Reed-Solomon (RS) codes. The watermark embedding and recovery processes can be applied without decompressing the compressed file completely. There is no perceptible degradation between the watermarked image and the original image. Experimental results show that the scheme is robust against some distortion attacks and JPEG lossy compression with different compression rates

Effective energy consumption scheduling in smart homes

Monthly expenditure on electricity by most households in South Africa take beyond acceptable percentage of their income. In order to keep the household energy expenditure below the energy poverty threshold, a daily electricity optimization problem is formulated using mixed integer linear programming (MILP) method. The energy optimization scheduling was carried out by a device called the Daily Maximum Energy Scheduling (DMES) device proposed to be incorporated into smart meters of households. The DMES algorithm was tested with household data set and was shown to be capable of ensuring that households spend less than 10% of their income on electricity bill monthly. This technique therefore, would be beneficial to consumers (for better financial savings and planning), utility (for effective energy and financial savings, and energy network planning) and cleaner environments as proposed for smart grid. Also, number of households in the nation living below the energy expenditure-based poverty threshold would increase.

Investigating Random Linear Coding from a Pricing Perspective

In this paper, we study the implications of using a form of network coding known as Random Linear Coding (RLC) for unicast communications from an economic perspective by investigating a simple scenario, in which several network nodes, the users, download files from the Internet via another network node, the sender, and the receivers as users pay a certain price to the sender for this service. The mean packet delay for a transmission scheme with RLC is analyzed and applied into an optimal pricing model to characterize the optimal admission rate, price and revenue. The simulation results show that RLC achieves better performance in terms of both mean packet delay and revenue compared to the basic retransmission scheme.

Wireless Service Pricing under Multiple Competitive Providers and Congestion-Sensitive Users

With the deregulation of telecommunication industry and the fast development of broadband wireless technologies, i.e., Wireless Mesh Network (WMN), WiFi (802.11g) and WiMAX (802.16), it can be imagined that in the future users can access Internet or other wireless services, e.g., telephony, through diverse wireless service providers (WSPs) and technologies. In this complex networking landscape, moving decision-making from access points to users is a path to achieving system scalability (Zemlianov & de Veciana, 2005). Thus, for users, it is increasingly the case that they have more freedom to choose among several WSPs who provide wireless services instead of being contractually tied to a single WSP. For example, a user wishing to access the Internet via a WiFi hotspot or access point (AP) may find him in a zone covered by several wireless access providers, or he may choose among different transmission platforms: WiFi, WiMAX, 3G, and so on. In such a market, in which multiple WSPs compete for users who are priceand congestion-sensitive, it is important to investigate the economic issues that arise due to the presence of multiple competing service providers. In such a competitive environment, all players are self-interested in a sense that their actions or reactions in response to others’ actions only focus on maximizing their own payoffs. From a WSP’s point of view, it has to compete for users with other WSPs while maximizing its profit. From a user’s point of view, he aims to maximize his compensated utility by choosing a WSP offering the best trade-off between quality of service (QoS) and price. Our primary goal is to understand how each WSP sets its price in the presence of price-sensitive and congestion-sensitive users and other competing WSPs to maximize its own profit. Note that we focus on the price setting problem among multiple WSPs instead of price discrimination among users. Thus we simply assume that the users are homogeneous in utility functions and willingness to pay. According to the current design of WMN architectures, a user’s requests will be routed to one AP or base station (BS) (in the IEEE802.16 standards APs of the IEEE 802.11 are called base stations) automatically so that the data flows generated by the user’s requests can take the most appropriate route in terms minimum hop count or other QoS metrics (i.e., bandwidth, end-to-end delay, and so on). However, from the user’s point of view, besides QoS, the price is also an important consideration when the user selects an AP or BS for wireless service delivery. It is generally accepted that the current wireless data network models are flawed in the sense that they fail to capture (Das et al., 2004): 12