The sum rate of spectrum-sharing in decentralized and self-organizing wireless networks is investigated in this paper. Such networks pose the following two fundamental challenges: 1) cochannel interference and 2) the ...
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The sum rate of spectrum-sharing in decentralized and self-organizing wireless networks is investigated in this paper. Such networks pose the following two fundamental challenges: 1) cochannel interference and 2) the hidden node problem. For a slotted shared wireless medium, where resources are partitioned into time-frequency slots, time-multiplexed receiver initiated busy burst (BB) transmissions solve these problems by establishing an exclusion region around an active receiver by means of receiver feedback. The size of this exclusion region is controlled by an interference threshold that determines whether a user is allowed to transmit on a specific time-frequency resource unit. We propose a novel approach for setting the interference thresholds based on a heuristic derived for a two-link network. First, for two-links, the optimum threshold value is derived that maximizes the sum rate. Second, for multiple links, the new heuristic threshold that only relies on locally available information is derived. It is demonstrated via simulations that heuristic thresholding achieves superior sum rate compared to a fixed system-wide threshold. To complement simulation results, an analytical approach is developed to approximate the probability density function (pdf) of the sum of the interferers in BB setting with fixed threshold with a cumulant-based shifted log normal fitting method.
In applications involving networks of sensor-equipped autonomous vehicles, it is crucial to have an energy-efficient communication protocol due to the limited on-board batteries. Unlike traditional sensor networks, ve...
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In applications involving networks of sensor-equipped autonomous vehicles, it is crucial to have an energy-efficient communication protocol due to the limited on-board batteries. Unlike traditional sensor networks, vehicle sensor networks typically consist of only a small number of nodes. We exploit this fact in our protocoldesign by optimizing specifically for these mobile small-scale networks. Our proposed solution, ConverSS, is a hybrid MAC/routing protocol that is energy-efficient for vehicle sensor networks. We have evaluated the protocol through simulation in ns-2 and verified its operation in a testbed network deployment. The results show that ConverSS is robust to real-world link impairments and that it outperforms common layered solutions by as much as a factor of 10.
Explicit congestion control (XCC) is emerging as one potential solution for overcoming limitations inherent to the current TCP algorithm, characterized by unstable throughput, high queuing delay, RTT-limited fairness,...
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Explicit congestion control (XCC) is emerging as one potential solution for overcoming limitations inherent to the current TCP algorithm, characterized by unstable throughput, high queuing delay, RTT-limited fairness, and a static dynamic range that does not scale well to high bandwidth delay product networks. In XCC, routers provide multibit feedback to sources, which, in turn, adapt throughput more accurately to the path bandwidth with potentially faster convergence times. Such systems, however, require precise knowledge of link capacity for efficient operation. In the presence of variable-capacity media, e.g., 802.11, such information is not entirely obvious or may be difficult to extract. We explore three possible algorithms for XCC which retain efficiency under such conditions by inferring available bandwidth from queue dynamics and test them through simulations with two relevant XCC protocols: XCP and RCP. Additionally, preliminary results from an experimental implementation based on XCP are presented. Finally, we compare our proposals with TCP and show how such algorithms outperform it in terms of efficiency, stability, queuing delay, and flow-rate fairness.
This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call...
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This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call for data generated by all source nodes. The goal is to determine an optimal set of storage nodes that minimizes overall communication cost. Prior work toward this problem assumed homogeneous channel cost, which may not be the case in many network environments. We generalize the optimal storage problem for a tree-structured network by considering heterogeneous channel costs. The necessary and sufficient conditions for the optimal solution are identified, and an algorithm that incurs a linear time cost is proposed. We have also conducted extensive simulations to validate the algorithm and to evaluate its performance.
Interference is an unavoidable property of the wireless communication medium and, in sensor networks, such interference is exacerbated due to the energy-starved nature of the network devices themselves. In the presenc...
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Interference is an unavoidable property of the wireless communication medium and, in sensor networks, such interference is exacerbated due to the energy-starved nature of the network devices themselves. In the presence of antagonistic interference, reliable communication in sensor networks becomes an extremely challenging problem that, in recent years, has attracted significant attention from the research community. This survey presents the current state of affairs in the formulation of theoretical models for adversarial interference in sensor networks and the different algorithmic remedies developed by the research community. There is a particular focus on jamming adversaries and Byzantine faults as these capture a wide range of benign faults as well as malicious attacks. The models in the literature are examined and contrasted with the aim of discerning the underlying assumptions that dictate analytical bounds with regards to feasibility and a number of performance metrics such as communication complexity, latency, and energy efficiency. Limitations are also highlighted with a focus on how various results impact real world applications and, conversely, how the current sensor network technology informs newer models. Finally, directions for future research are discussed.
With the success of lnternet video-on-demand (VoD) streaming services, the bandwidth required and the financial cost incurred by the host of the video server becoming extremely large. Peer-to-peer (P2P) networks and p...
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With the success of lnternet video-on-demand (VoD) streaming services, the bandwidth required and the financial cost incurred by the host of the video server becoming extremely large. Peer-to-peer (P2P) networks and proxies are two common ways for reducing the server workload. In this paper, we consider a peer-assisted Internet VoD system with proxies deployed at domain gateways. We formally present the video caching problem with the objectives of reducing the video server workload and avoiding inter-domain traffic, and we obtain its optimal solution. Inspired by theoretical analysis, we develop a practical protocol named PopCap for Internet VoD services. Compared with previous work, PopCap does not require additional infrastructure support, is inexpensive, and able to cope well with the characteristic workloads of Interact VoD services. From simulationbased experiments driven by real-world data sets from YouTube, we find that PopCap can effectively reduce the video server workload, therefore provides a superior performance regarding the video server's traffic reduction.
Object tracking in wireless sensor networks is to track mobile objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object's move. Existing tree...
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Object tracking in wireless sensor networks is to track mobile objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object's move. Existing tree construction algorithms all require a mobility profile that characterizes the movement statistics of the target object. Mobility profiles are generally obtained based on historical running traces. The contribution of this work is twofold. We first show that the problem of finding an optimal message report tree that requires the least amount of report messages is NP-hard. We then propose analytic estimates of mobility profiles based on Markov-chain model. This profiling replaces an otherwise experimental process that generates and analyzes running traces. Simulation results show that the analytic profiling works well and can replace costly statistical profiling without noticeable performance degradation.
Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Althoug...
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Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.
This work started out with our discovery of a pattern of throughput distributions among links in IEEE 802.11 networks from experimental results. This pattern gives rise to an easy computation method, which we term bac...
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This work started out with our discovery of a pattern of throughput distributions among links in IEEE 802.11 networks from experimental results. This pattern gives rise to an easy computation method, which we term back-of-the-envelop (BoE) computation. For many network configurations, very accurate results can be obtained by BoE within minutes, if not seconds, by simple hand computation. This allows us to make shortcuts in performance evaluation, bypassing complicated stochastic analysis. To explain BoE, we construct a theory based on the model of an "ideal CSMA network" (ICN). The BoE computation method emerges from ICN when we take the limit c -> 0, where c is the ratio of the mean backoff countdown time to the mean transmission time in the CSMA protocol. Importantly, we derive a new mathematical result: the link throughputs of ICN are insensitive to the distributions of the backoff countdown time and transmission time (packet duration) given the ratio of their means c. This insensitivity result explains why BoE works so well for practical 802.11 networks, in which the backoff countdown process is one that has memory, and in which the packet size can be arbitrarily distributed. Our results indicate that BoE is a good approximation technique for modest-size networks such as those typically seen in 802.11 deployments. Beyond explaining BoE, the theoretical framework of ICN is also a foundation for fundamental understanding of very-large-scale CSMA networks. In particular, ICN is similar to the Ising model in statistical physics used to explain phenomena arising out of the interactions of a large number of entities. Many new research directions arise out of the ICN model.
Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service r...
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Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e. g., coverage holes caused by death of nodes) or physical environments (e. g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.
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