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Abstract — Energy constraints in a wireless sensor network are crucial issues critically affecting the network lifetime and connectivity. To realize true energy saving in a wireless environment, the time varying property of the wireless channel should also be taken into account. Unfortunately, this factor has long been ignored in most existing state-of-the-art energy saving protocols. Neglecting the effects of varying channel quality can lead to an unnecessary waste of precious battery resources, and, in turn, can result in the rapid depletion of sensor energy and partitioning of the network. In this paper, we propose a channel adaptive energy management protocol, called CAEM, that can exploit this time varying nature of the wireless link. Specifically, CAEM leverages on the synergistically cross-layer interaction between physical and MAC layers. Thus, each sensor node can intelligently access the wireless medium according to the current wireless link quality and the predicted traffic load, to realize an efficient utilization of the energy. Extensive simulation results indicate that CAEM can achieve as much as 40 % reduction in energy dissipation compared with traditional protocols without channel adaptation. Index Terms—mobile computing, wireless sensor networks, power saving, channel state dependent, adaptive cross-layer protocols. I.

As wireless sensor networks mature, they are increasingly being used in real-time applications. Many of these applications require reliable transmission within latency bounds. Achieving this goal is very difficult because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3,600,000 packets transmission per link, we propose a scheduling algorithm that produces latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst case burst lengths over all links in the route. A testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100 % reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high quality links.

Abstract—The subject of traffic policing for computer communication networks has been studied extensively in the literature. However, the constant development of new multimedia applications which are “greedy ” in terms of bandwidth and Quality of Service requirements calls for new approaches to the traffic policing problem. In this work, we introduce a new video model for single H.263 videoconference sources and we use it in order to propose a new traffic policing approach for wireless videoconference traffic. We study well-known traffic policing mechanisms which still present interesting, unsolved problems when servicing video traffic and propose, to the best of our knowledge, for the first time in the relevant literature that the token generator is based on a traffic model and not on a fixed rate. The proposed approach shows significant improvement in the results obtained by all the traffic policing mechanisms, and hence, shows that dynamic traffic policing can provide much higher efficiency than the widely used static approach. Index Terms—Traffic policing, token bucket, traffic modeling, wireless videoconference, H.263 video encoding. Ç 1

Abstract—Energy efficiency has become increasingly important to mobile systems on which wireless interfaces are among the largest power consumers. While existing physical layer power optimization mostly focuses on improving the transmission efficiency, our recent work has showed that wireless interfaces can spend most of its time and energy in very short idle periods between transmitting two packets [9]. In this work, we present a physical layer optimization method, drowsy transmission, which explicitly considers the power cost of such idle periods in physical layer power optimization through joint power control/rate selection and power management. We provide a control theoretical formulation of the optimization problem and present a dynamic programming based solution and its approximation that is close form and practical. We further offer an on-line learning technique to cope with unknown channel and traffic. Using a power model from a commercial wireless network interface card, we demonstrate that drowsy transmission can reduce the energy per bit by 70 % and 40 % in comparison to power control/rate selection-based optimization and optimization with disjoint power control/rate selection and power management, respectively. Moreover, the achieved energy per bit is very close to the theoretical lower bound. Our evaluation shows that the proposed on-line learning technique can assess the channel and approach the performance under pre-known channel in as short as 200ms. We also show that our optimization introduces negligible packet delays. I.