There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

COVID-19 Dashboard

Power Aware Routing in Mobile Ad Hoc Networks

Hawaii International Conference on System Sciences

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

An energy-efficient path determination strategy for mobile data collectors in wireless sensor network

Mobile sink based fault diagnosis scheme for wireless sensor networks

Non-parametric sequence-based learning approach for outlier detection in IoT

Internet of Things (IoT) devices are composed of millions of sensors that continuously sense environmental parameters which are effectively fused to gain insights on a particular area or region based on which desired actions are taken. However, before any fusion of the data takes place, checking the quality of data is of paramount importance as these data may also be contaminated with outliers which limit their overall efficiency. Outliers or in other words, any abrupt change in values can be either due to a drastic change in environment (Event) or because of any malfunctioning of the sensor (Error). This paper introduces an IoT architecture to detect the occurrence of both Error and Event in a forest environment with the help of four statistical models, i.e., Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM) and Linear Discriminant Analysis (LDA). We take into account the spatial and temporal dependencies of the data as well. Simulation results show that the models can effectively detect both types of outliers with upto 100% accuracy for Error detection and upto 98.51% in case of Event detection. Also, the results provide evidence that using spatial and temporal parameters in event detection can have a positive influence on the prediction accuracy which was observed to increase from 81.68% to 96.53% using RF. Among the four models, RF is seen to outperform the others.

Outlier detection in sensed data using statistical learning models for IoT

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, particle swarm optimisation has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on ant-colony optimisation has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab simulations.

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Indrajit Banerjee

Papers

An energy-efficient path determination strategy for mobile data collectors in wireless sensor network

Mobile sink based fault diagnosis scheme for wireless sensor networks

Non-parametric sequence-based learning approach for outlier detection in IoT

Outlier detection in sensed data using statistical learning models for IoT

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network