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In wireless sensor networks, geographic adaptive fidelity (GAF) is one of the most popular energy-aware routing protocols. It conserves energy by identifying equivalence between sensors from a routing perspective and then turning off unnecessary sensors, while maintaining the connectivity of the network. Nevertheless, the traditional GAF still cannot reach the optimum energy usage since it needs more number of hops to transmit data packets to the sink. As a result, it also leads to higher packet delay. In this paper, we propose a modified version of GAF to minimize hop count for data routing, called two-level GAF (T-GAF). Furthermore, we use a generalized version of GAF called Diagonal-GAF (DGAF) where two diagonal adjacent grids can also directly communicate. It has an advantage of less overhead of coordinator election based on the residual energy of sensors. Analysis and simulation results show significant improvements of the proposed work comparing to traditional GAF in the aspect of total hop count, energy consumption, total distance covered by the data packet before reaching the sink, and packet delay. As a result, compared to traditional GAF, it needs 40% to 47% less hop count and consumes 27% to 35% less energy to extend the network lifetime.

Abstract. In wireless sensor networks, clustering provides an effective way of organising the sensor nodes to achieve load balancing and increasing the lifetime of the network. Unequal clustering is an extension of common clustering that exhibits even better load balancing. Most existing approaches do not consider node density when clustering, which can pose significant problems. In this paper, a fuzzy-logic based cluster head selection approach is proposed, which considers the residual energy, centrality and density of the nodes. In addition, a fuzzy-logic based clustering range assignment approach is used, which considers the suitability and the position of the nodes in assigning the clustering range. Furthermore, a weight function is used to optimize the selection of the relay nodes. The proposed approach was compared with a number of well known approaches by simulation. The results showed that the proposed approach performs better than the other algorithms in terms of lifetime and other metrics.

In the current work, we have proposed a parallel algorithm for the recognition of Epileptic Spikes (ES) in EEG. The automated systems are used in biomedical field to help the doctors and pathologist by producing the result of an inspection in real time. Generally, the biomedical signal data to be processed are very large in size. A uniprocessor computer is having its own limitation regarding its speed. So the fastest available computer with latest configuration also may not produce results in real time for the immense computation. Parallel computing can be proved as a useful tool for processing the huge data with higher speed. In the proposed algorithm 'Data Parallelism' has been applied where multiple processors perform the same operation on different part of the data to produce fast result. All the processors are interconnected with each other by an interconnection network. The complexity of the algorithm was analyzed as Θ((n + δn) / N) where, 'n' is the length of the input data, 'N' is the number of processor used in the algorithm and 'δn' is the amount of overlapped data between two consecutive intermediate processors (IPs). This algorithm is scalable as the level of parallelism increase linearly with the increase in number of processors. The algorithm has been implemented in Message Passing Interface (MPI). It was tested with 60 min recorded EEG signal data files. The recognition rate of ES on an average was 95.68%.

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