Bijendra Kumar

NEECP: Novel energy-efficient clustering protocol for prolonging lifetime of WSNs

In this paper, a novel energy-efficient clustering protocol (NEECP) for increasing the network lifetime in wireless sensor networks is proposed. This technique selects the cluster heads in an effective way with an adjustable sensing range and performs data aggregation using chaining approach. It also avoids transmission of redundant data by using a redundancy check function for improving the network lifetime. It is implemented by considering the data with aggregation and without aggregation. The NEECP without aggregation increases the network lifetime by 59.76 and 7.17% as compared with the hybrid energy-efficient distributed (HEED) and intra-balanced low-energy adaptive clustering hierarchy (IBLEACH), respectively. It increases the network lifetime by 122.92 and 49.53% over the HEED and IBLEACH, respectively, while considering data aggregation.

Request based data delivery in video-on-demand services

In unified video-on-demand (UVoD) architecture, the logical channels are classified as multicast and unicast channels. The multicast channels provide services periodically and are independent of the user requests, whereas the unicast channels are used to provide services to the individual users. In that architecture, the optimal number of unicast channels has been reported to be independent of the request arrival rate. This result seems to be less accurate as the number of logical channels is affected by the request arrival rate. In this paper, we propose a more accurate study in which the number of unicast channels is shown to be explicitly dependent on the request arrival rate. In addition, for providing immediate services to the users, the number of unicast channels is much less than that of the UVoD architecture. The other important point of this study is that it can provide services for both less popular and more popular videos. However, for more popular videos all channels should be multicast channels

Analytical study of load scheduling algorithms in cloud computing

Scheduling enables the cloud in balancing the large amount of load present in the system for faster computation. It plays a vital and significant part in the execution of the load in the various heterogeneous systems. The scheduling portrays a selection of resources for the tasks for better resource utilization. This paper differentiates the various load scheduling algorithms applied in the various heterogeneous systems in detail. It plays a key role in larger resource utilization and handling. This paper defines the basic cloud computing fundamentals and the concept of load balancing i.e., scheduling of load in cloud. The applied load scheduling algorithms are elaborated and surveyed extensively.

Optimum deployment of sensors in WSNs

Ant Colony Optimization (ACO) is one of the important techniques for solving optimization problems. It has been used to find locations to deploy sensors in a grid environment [12], in which the targets, called point of interest (PoI), are located on grid points in a square grid. The locations of sensors, which are grid points, are determined by considering the sink location as the starting point for deploying sensors. Though that work provides optimum number of sensors to cover all targets with respect to the given sink location, yet it does not provide which sink location provides minimum number of sensors to cover the targets. In this paper, we use ACO technique and find the sink location for which the number of sensors is minimum among all available locations in the grid. In our algorithm, we compute sum of distances of the targets from that sensor, which are in its range. Then we add these sums for all sensors in the grid. This distance corresponds to the given sink location. We repeat same process for computing the distance by changing the sink location in the grid. We choose that sink location for which the distance is minimum and this sink location requires minimum number of sensors to cover all targets. We carry out simulations to demonstrate the effectiveness of our proposed work.

Video Data Delivery using Slotted Patching

In the request-based scheme, a patching channel is initiated on arrival of a request in an interarrival time of two adjacent multicast channels. If two requests are received in a short duration in an interarrival time, two patching channels are needed to service them. Both these channels deliver almost the same data, thus, wasting the bandwidth. This problem may be addressed in two ways: using higher level patching technique, or forcing the first request to wait for the second one. Using higher level patching technique makes the system more complex and thus usually not preferred. In the second case, the bandwidth usage certainly reduces, but the length of the waiting time by the first request for the second request is the main issue. In this paper, this issue is addressed by dividing the video duration into uniform time slots such that there is at least one request in a time slot for initiating the patching channel. The proposed scheme is named as Video Data Delivery using Slotted Patching. In this scheme, the patching channels download much less video data than the request-based scheme and thus require less bandwidth. This scheme may not provide immediate services to all users unlike the request-based scheme, but the users waiting can be made arbitrarily small without using much resources unlike the request-based scheme.

3-Tier Heterogeneous Network Model for Increasing Lifetime in Three Dimensional WSNs

Homogeneous algorithms assume that the entire sensor node equipped with equal amount of energy. In this paper, a network model has been proposed which incorporate heterogeneity in term of their energy. The term heterogeneity means nodes equipped with dissimilar amount of energy. This model contains three tier node heterogeneity namely tier-1, tier-2, and tier-3 heterogeneity. We assume that nodes are equipped in three dimensions, not mobile, and randomly distributed. It performance is compared with 3D-ALBP, called 3D-hetALBP. Finally, the simulation results demonstrate that our proposed heterogeneous algorithm is more effective in prolonging the network lifetime compared with 3D-ALBP.

Segmented patching broadcasting protocol for video data

In adaptive segment-based patching scheme, the video is divided into fixed number of segments, which are transmitted over the server channels. For efficient transmission of the video segments, the server channels are classified into two types - regular and patching channels. A regular channel generally transmits fixed number of segments and a patching channel helps transmitting those segments that cannot be provided by any regular channel to the users. The number of segments transmitted by the first regular channel is decided by the number of regular channels that are allocated to the video by the video server. Other regular channels transmit pre-specified number of segments. This scheme estimates the bandwidth of the patching channels based on the requests received at the video server in terms of fixed time intervals, called time slots. The bandwidth estimation in this scheme is less accurate because for multiple requests received in a time slot more than one patching channels are used. Second, the probability distribution considered in this scheme does not satisfy the basic rule, i.e., the sum of all probabilities is not 1. In this paper, we address these issues and propose a new protocol named as Segmented Patching Broadcasting Protocol for Video Data. The average server bandwidth allocated to the patching channels is much less as compared to the adaptive segment-based patching scheme because only one patching channel is sufficient for any number of requests received in a time slot.

A New Balanced Particle Swarm Optimisation for Load Scheduling in Cloud Computing

The cloud computing is an augmentative and progressive paradigm that supports a huge amount of characteristics. It demands the optimal allocation of resources to the tasks present in the virtual machines (VMs) system using load scheduling algorithms. The basic objective of load scheduling is to avoid system overloading and thereby achieve higher throughput by maximising VM utilisation along with cost stabilisation. The first come first serve and min–min approaches allocate the load in a static manner and resources are left underutilised. The particle swarm optimisation obtains the motivation from the social behaviour of the flock of birds. It analyses various approaches for load scheduling. The paper proposes an improved balanced load scheduling approach based on particle swarm optimisation (BPSO) to minimise total transfer time and total cost stabilisation. The proposed BPSO approach is compared with the existing approaches used for load scheduling in cloudlets. The efficiency in terms of the transfer ti...

Multilevel heterogeneous network model for wireless sensor networks

The lifetime of a network can be increased by increasing the network energy. The network energy can be increased either increasing the number of sensors or increasing the initial energy of some sensors without increasing their numbers. Increasing network energy by deploying extra sensors is about ten times costlier than that using some sensors of high energy. Increasing the initial energy of some sensors leads to heterogeneous nodes in the network. In this paper, we propose a multilevel heterogeneous network model that is characterized by two types of parameters: primary parameter and secondary parameters. The primary parameter decides the level of heterogeneity by assuming the values of secondary parameters. This model can describe a network up to nth level of heterogeneity (n is a finite number). We evaluate the network performance by applying the HEED, a clustering protocol, on this model, naming it as MLHEED (Multi Level HEED) protocol. For n level of heterogeneity, this protocol is denoted by MLHEED-n. The numbers of nodes of each type in any level of heterogeneity are determined by the secondary model parameter. The MLHEED protocol (for all level heterogeneity) considers two variables, i.e., residual energy and node density, for deciding the cluster heads. We also consider fuzzy implementation of the MLHEED in which four variables are used to decide the cluster heads: residual energy, node density, average energy, and distance between base station and the sensor nodes. In this work, we illustrate the network model up to seven levels (