A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

ENROUTE: An Entropy Aware Routing Scheme for Information-Centric Networks (ICN)

With the exponential growth of end users and web data, the internet is undergoing the change of paradigm from a user-centric model to a content-centric one, popularly known as information-centric networks (ICN). Current ICN research evolves around three key-issues namely (i) content request searching, (ii) content routing, and (iii) in-network caching scheme to deliver the requested content to the end user. This would improve the user experience to obtain requested content because it lowers the download delay and provides higher throughput. Existing researches have mainly focused on on-path congestion or expected delivery time of a content to determine the optimized path towards custodian. However, it ignores the cumulative effect of the link-state parameters and the state of the cache, and consequently it leads to degrade the delay performance. In order to overcome this shortfall, we consider both the congestion of a link and the state of on-path caches to determine the best possible routes. We introduce a generic term entropy to quantify the effects of link congestion and state of on-path caches. Thereafter, we develop a novel entropy dependent algorithm namely ENROUTE for searching of content request triggered by any user, routing of this content, and caching for the delivery this requested content to the user. The entropy value of an intra-domain node indicates how many popular contents are already cached in the node, which, in turn, signifies the degree of enrichment of that node with the popular contents. On the other hand, the entropy for a link indicates how much the link is congested with the traversal of contents. In order to have reduced delay, we enhance the entropy of caches in nodes, and also use path with low entropy for downloading contents. We evaluate the performance of our proposed ENROUTE algorithm against state-of-the-art schemes for various network parameters and observe an improvement of 29–52% in delay, 12–39% in hit rate, and 4–39% in throughput

Fuzzy logic election of node for routing in WSNs

Sensor nodes of Wireless Sensor Networks (WSNs) are resource constraints in energy, memory, processing and communication bandwidth. Since they are operated by battery, their life span is limited. Specially, energy conservation is very important issue in the WSN, because it directly affects the life of the node as well as the entire network. Here, we develop a new way of electing a node among many trustworthy nodes for routing processes. This method consumes the energies of network nodes based on Fuzzy logic applied on their residual energy, trust level and distance from the Base Station. The proposed method elects one indispensible node for participating in routing among many worthy nodes. Hence, this method of election of node for routing in WSN sees the conservation of nodes energies go by very smooth and justifying, thereby increasing the life of the WSN. © 2012 IEEE

Input-Adaptive and Quality-Configurable Approximate Computing: A Full-System Perspective

Approximate computing is an emerging design paradigm that leverages the intrinsic resilience of applications to execute computations approximately, and more efficiently, leading to improvements in energy consumption or performance. A key challenge in approximate computing is that the extent to which computations can be approximated varies significantly from application to application, as well as across inputs for a single application. This makes quality-configurability (modulation of the energy vs. quality trade-off of applications at runtime) and input-adaptivity (tuning the degree of approximation based on individual inputs) essential for obtaining significant energy savings while retaining output quality at acceptable levels. The first part of this research explores input-adaptive and quality-configurable approximate computing at various layers of design abstraction. Most prior work in approximate computing deals with approximate computation only. However, in a typical embedded system, computation is only one component of the entire system. In addition to the computation subsystem, we also have other subsystems such as memory, sensing, and communication. To maximize the energy benefits, it is essential to focus not only on approximate computation but also investigate approximations in these other subsystems. Towards this goal, the second part of this research first explores approximate memory by proposing a systematic methodology to construct a quality configurable approximate DRAM. Finally, this thesis proposes a full-system approximation methodology that synergistically tunes the different subsystem-level approximation knobs to maximize system-level energy savings for a specified target quality constraint. As an embodiment of this principle, a case-study of an approximate smart-camera system is presented and demonstrated