Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks. Various visual sensor platforms have been developed with the aim of integrating visual data to wireless sensor applications. The objective of this article is to survey current visual sensor platforms according to in-network processing and compression/coding techniques together with their targeted applications. Characteristics of these platforms such as level of integration, data processing hardware, energy dissipation, radios and operating systems are also explored and discussed.
This is a position paper on our views on security aspects of Wireless Multimedia Sensor Networks (Secure WMSNs). It is meant to serve as a brief survey. But, more importantly, it gives a perspective on how we foresee the future of this research area, its main challenges, and its future trends. We believe that this paper will spur new discussions and research ideas among the researchers from both the industry and the academic world.
Contextual privacy in Wireless Sensor Networks (WSNs) is concerned with protecting contextual information such as whether, when, and where the data is collected. In this context, hiding the existence of a WSN from adversaries is a desirable feature. One way to mitigate the sensor nodes' detectability is by limiting the transmission power of the nodes (i.e., the network is operating in the stealth mode) so that adversaries cannot detect the existence of the WSN unless they are within the sensing range of the WSN. Position dependent transmission power adjustment enables the network to maintain its level of stealth while allowing nodes farther from the network boundary to use higher transmission power levels. To mitigate the uneven energy dissipation characteristic, nodes that cannot dissipate their energies on communications reduce the amount of data they generate through computation so that the relay nodes convey less data. Dynamic data compression/decompression strategies reduce the amount of data to be communicated, thus, they achieve better energy savings when compared to static compression/decompression of data in which the data is always compressed independently of the power transmission strategy. In this study, we investigate various data compression strategies to maximize the lifetime of WSNs employing contextual privacy measures through a novel Mathematical Programming framework.
In certain surveillance applications it is imperative that the deployed Wireless Sensor Network (WSN) is not detected by the adversaries before the intruding party is detected by the WSN (i.e., the WSN is in the stealth mode of operation). Limiting the transmission ranges of sensor nodes is an option to mitigate the compromising privacy of the WSN (i.e., data communication within the WSN is not detected from far away). However, using all sensor nodes with minimal energy transmission level has devastating effects on the network lifetime because some nodes acting as relays are heavily burdened by conveying the data flowing from an unproportionately high number of sensor nodes. Such an approach will lead to the premature death of certain subset of sensor nodes. Alternatively, sensor nodes' transmission ranges can be limited as a function of their distance to the network border. Even under this policy a subset of the nodes become hotspots. On the other hand, nodes close the border cannot dissipate their energies completely because they cannot relay much data due the limits imposed on their transmission ranges. One possible solution to mitigate the uneven energy dissipation characteristic is to let the nodes that cannot dissipate their energies on communications reduce the amount of data they generate through computation so that the relay nodes convey less data. In this study we create a novel Linear Programming (LP) framework to model the energy cost of contextual privacy and multi-level data compression in WSNs.
As the evolution of multi-core multi-threaded processors continues, the complexity demanded to perform an extensive trade-off analysis, increases proportionally. Cycleaccurate or trace-driven simulators are too slow to execute the large amount of experiments required to obtain indicative results. To achieve a thorough analysis of the system, software benchmarks or traces are required. In many cases when an analysis is needed most, during the earlier stages of the processor design, benchmarks or traces are not available. Analytical models overcome these limitations but do not provide the fine grain details needed for a deep analysis of these architectures.In this work we present a new methodology to abstract processor architectures, at a level between cycle-accurate and analytical simulators. To apply our methodology we use queueing modeling techniques. Thus, we introduce Q-MAS, a queueing based tool targeting a real chip (the UltraSPARC T2 processor) and aimed at facilitating the quanti¿cation of trade-offs during the design phase of multi-core multi-threaded processor architectures. The results demonstrate that Q-MAS, the tool that we developed, provides accurate results very close to the actual hardware, with a minimal cost of running what-if scenarios.Keywords-component; Fine grain modeling; a methodology to build simulators; a simulation tool for multi-levels of shared resource architecture modeling; UltraSPARC T2, queueing modeling; "whatif"s for CPU architecture.
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