In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

/pdf/energy-conservation-in-wireless-sensor-networks-a-survey-1bl7d2obnm.pdf

Energy conservation in wireless sensor networks: A survey

Information Theory and Reliable Communication

Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted

/pdf/gesture-recognition-a-survey-3j4b8mpnm4.pdf

Gesture Recognition: A Survey

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

/pdf/power-management-in-energy-harvesting-sensor-networks-53e5l8ne72.pdf

Power management in energy harvesting sensor networks

Power Aware Routing in Mobile Ad Hoc Networks

Wireless networks have historically considered support for mobile elements as an extra overhead. However, recent research has provided means by which network can take advantage of mobile elements. Particularly, in the case of wireless sensor networks, mobile elements are deliberately built into the system to improve the lifetime of the network, and act as mechanical carriers of data. The mobile element, which is controlled, visits the nodes to collect their data before their buffers are full. It may happen that the sensor nodes are sampling at different rates, in which case some nodes need to be visited more frequently than others. We present this problem of scheduling the mobile element in the network, so that there is no data loss due to buffer overflow. We prove that the problem is NP-complete and give an ILP formulation. We give some practical algorithms, and compare their performances.

Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines

Wireless networks have historically considered support for mobile elements's an extra overhead. However, recent research has provided the means by which a network can take advantage of mobile elements. Particularly in the case of wireless sensor networks, mobile elements can be deliberately built into the system to improve the lifetime of the network and act as mechanical carriers of data. The mobile element, whose mobility is controlled, visits the nodes to collect their data before their buffers are full. In general, the spatio-temporal dynamics of the sensed phenomenon may require sensor nodes to collect samples at different rates, in which case, some nodes need to be visited more frequently than others. This work formulates the problem of scheduling the mobile element in the network so that there is no data loss due to buffer overflow. The problem is shown to be NP-complete and an integer-linear-programming formulation is given. Finally, some computationally practical algorithms for a single mobile and for the case of multiple mobiles are presented and their performances compared

Mobile Element Scheduling with Dynamic Deadlines

A solid state non-volatile memory unit. The memory unit includes a multi-level solid state non-volatile memory array adapted to store data characterized by a first number of digital levels. The memory unit also includes an analog-to-digital converter having an input and an output. The input of the analog-to-digital converter is adapted to receive data from the multi-level solid state non-volatile memory array. The output of the analog-to-digital converter is adapted to output a digital signal characterized by a second number of digital levels greater than the first number of digital levels.

/pdf/method-and-system-for-error-correction-in-flash-memory-49ziiavulu.pdf

Method and system for error correction in flash memory

Caching is often used in content delivery networks as a mechanism for reducing network traffic Recently, the technique of coded caching was introduced whereby coding in the caches and coded transmission signals from the central server were considered Prior results in this area demonstrate that carefully designing the placement of content in the caches and designing appropriate coded delivery signals from the server allow for a system where the delivery rates can be significantly smaller than conventional schemes However, matching upper and lower bounds on the transmission rate have not yet been obtained In this paper, we derive tighter lower bounds on the coded caching rate than were known previously We demonstrate that this problem can equivalently be posed as a combinatorial problem of optimally labeling the leaves of a directed tree Our proposed labeling algorithm allows for significantly improved lower bounds on the coded caching rate Furthermore, we study certain structural properties of our algorithm that allow us to analytically quantify improvements on the rate lower bound for general values of the problem parameters This allows us to obtain a multiplicative gap of at most four between the achievable rate and our lower bound

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1168&context=ece_pubs

Improved Lower Bounds for Coded Caching

http://home.eng.iastate.edu/%7enamrata/gatech_web/HandGesture.pdf

Aditya Ramamoorthy

Papers

Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines

Mobile Element Scheduling with Dynamic Deadlines

Method and system for error correction in flash memory

Improved Lower Bounds for Coded Caching

Recognition of dynamic hand gestures