Bibudhendu Pati

EEOA: Improving energy efficiency of mobile cloudlets using efficient offloading approach

Cyber Foraging concepts allow the users to take the benefits of low latency cloud services by offloading the computational requests to the nearest service provider instead of offloading the computational request services to service providers over Wide Area Network (WAN). The cloudlets are major components of cyber foraged systems and are deployed in the physical proximity of the users, where the cloudlets get connected with users through LAN or Wi-Fi. In the mobile state, the cloudlets are deployed over the mobile devices such as smart phones. The performance of mobile cloudlets depends upon the battery life of mobile devices, which is limited. In this work, we propose an energy efficient offloading approach EEOA, which can help in effective resource utilization of mobile cloudlets for optimal energy usage. Based on the simulation results, we observed that the EEOA helps in optimal resource utilization of mobile devices and also minimizes the energy consumption for mobile devices.

A Novel Approach for Real-Time Data Management in Wireless Sensor Networks

Wireless sensor networks (WSNs) become an emerging area of research now-a-days. It plays an important role to support a wide range of applications. In this work, we propose an architecture for data management in WSNs where in each cluster, sensor nodes transfer data to their cluster-head (CH) and among the CHs one CH is selected based on the highest energy label. The CH aggregates data and reduces the size of the data to be transmitted to the base station. We have also proposed an algorithm which determines how fast the data is transferred from the source node to the base station.

ECHSA: An Energy-Efficient Cluster-Head Selection Algorithm in Wireless Sensor Networks

In Wireless Sensor Networks WSNs a key issue is the limited battery power of sensor nodes. To increase the network lifetime is a great challenge where different nodes have different energy labels. To work with this challenge we propose an Energy-Efficient Cluster-Head Selection Algorithm ECHSA based on Nash Equilibrium NE decision of game theory where, each cluster in the network acts as a player and each player chooses his best strategy followed by other players. We have also compared ECHSA with existing protocols. The simulation results show increase in performance of our proposed approach as compared to the existing approaches.

S2S: A Novel Approach for Source to Sink Node Communication in Wireless Sensor Networks

In Wireless Sensor Networks WSNs sensor nodes are deployed in various geographical areas. But, the main problem is the data collection from source nodes to the sink node in an energy efficient way and in data collection scenario it is also a challenging task to reduce inter-cluster communication cost which can balance the network traffic. To work with these challenges, in this work, we propose a source to sink node S2S communication algorithm where to reduce the communication overhead and to minimize the delay we used Cluster-HeadCH-CH communication method where one CH forms coalition to another CH based on the distance of the sink node from each CH. The simulation results indicate the better performance of our approach as compared to the existing approaches and the results are validated through MATLAB.

Energy Competent Routing Protocol Design in MANET with Real Time Application Provision

With the increase of mobile activities in technological environment, it is a challenge to develop and maintain robust networking system with provision of high connectivity. In this context, Mobile Adhoc Network MANET is a feasible solution because it is dynamic in nature and the self-configured nodes of MANET can communicate intelligently in adverse and dynamic mobile situations. Though Adhoc on Demand Distance Vector AODV Routing protocol handles the networking issues efficiently, still power and delay efficiency has always been a challenging task in MANET due to the resource constrained nodes which are supplied with limited battery power during the operation in the network. Hence while performing various networking activities; gradually the battery power reduces resulting in delay in processing of packet and forwarding them to the next node during transmission. This causes overall end to end delay in the total transmission. Therefore many researches are still going on in the direction of designing robust and reliable protocols. In this paper the authors have developed a robust and energy efficient routing protocol for MANET with real time support. Their approach is different as it calculates the remaining residual battery power, bounded delay and packet processing rate of the intermediate node before selecting a node to forward the packet in the direction of destination. The cost function calculated using the above three factors along with the condition of congested node is also checked to correctly choose a load balanced path from source to destination. Simulation results show that the proposed protocol performs better than the conventional AODV protocol in terms of better network life time, throughput, delay and packet delivery ratio.

Big Data and Cyber Foraging: Future Scope and Challenges

This chapter on “Big Data and Cyber Foraging: Future Scope and Challenges” mainly focuses on the development of cyber foraging systems. This chapter introduces the concept of cloudlets and the role of cloudlets in cyber foraging systems as well as discusses about the working and limitations of cloudlets. This chapter also explores the new architectures where the cloudlets can be helpful in providing Big data solutions in areas with less Internet connectivity and where the user device disruption is high. Different aspects in which the cloudlets can be used for managing and processing of Big data are also highlighted. The chapter then deals with different applications of cloudlets for Big data and focuses on the details of the existing work done with cyber foraging systems to manage different characteristics of Big data. Finally, this chapter explores the future scope and challenges of using cloudlets in the context of Big data.

eCloud: An Efficient Transmission Policy for Mobile Cloud Computing in Emergency Areas

Due to the less resources and battery power of mobile devices or intermittent connectivity between mobile devices and cloud, the users may face huge difficulties. There are a very few work have been proposed which can solve these problems. But, to select best cloud for mobile devices as well as to minimize the transmission latency upon mobility of mobile nodes, in this work an efficient transmission policy for Mobile Cloud Computing (MCC) named as eCloud is proposed. In eCloud, mobile nodes can select their best cloud for sending requests upon mobility of mobile nodes. eCloud can solve the problem of battlefield situation or any emergency condition like earthquake or terrorists attack.

E 3 M: An Energy Efficient Emergency Management System using mobile cloud computing

Mobile devices play a vital role for handling emergency situations. During emergency, it is very difficult to collect necessary information from the mobile devices if there is unavailability of networks. In this work, an Energy Efficient Emergency Management System named as E3M has been proposed. E3M supports peer-to-peer communication between mobile devices if a mobile device does not find any suitable networks for communication with the cloud. To make E3M energy efficient, it selects best available mobile device by forming coalition among devices. One device among them is selected for data communication based on the residual energy of devices if there is no target device found in the range of the source device. The simulation results indicate that E3M is suitable for emergency conditions where, mobile devices do not find any suitable networks. That means E3M also works in the absence of any network by considering wireless mesh network technology.

CAB: Cloudlets as Agents of Cloud Brokers

Cloud computing provides a variety of services to the users and charges the users based on the usage. The development of cyber foraging concept allows the users to offload the computationally intensive services to the nearest data centers for getting instant results. The Internet WAN latency acts as a major constraint to get instant results for computationally intensive services. The development of cloudlets in recent years allows the cloud users to offload computationally intensive operations to cloudlets to get the results, where the cloudlets get connected with users through LAN or Wi-Fi. The cloudlets are generally deployed based on the geographical area and density of users. Cloud brokers act as an intermediate layer between the cloud and the users. The brokers select best set of services from different cloud providers and offer these services to the users. In this work, we propose an approach named as CAB( Introducing Cloudlets as Agents of Cloud Brokers). Here we assume that cloud brokers mainly focus on providing the computationally intensive services or services with strict time deadline specified in Service Level Agreement(SLA) to the users through cloudlets. The cloud brokers deploy cloudlets based on user requirements for computationally intensive services or strict SLAs. In this work, we also introduce a pricing scheme for cloud brokers and an algorithm which can optimize the profit for cloud brokers. Based on the simulation, we observed that the profit for brokers increases when the number of cloudlets increase for an area or the processing and memory resources increase for a cloudlet which also causes more faster execution of services requested from the users.

Malware Detection in Big Data Using Fast Pattern Matching: A Hadoop Based Comparison on GPU

In big data environment, hadoop stores the data in distributed file systems called hadoop distributed file system and process the data using parallel approach. When the cloud users store unstructured data in cloud storage, it becomes very important for cloud providers to secure those data. To provide malware security, cloud service providers should scan the whole contents of the database, which is a very time intensive job. It may even take days to complete the tasks. The main aim of the proposed work is to reduce the processing time by introducing Graphics Processing Unit (GPU) in hadoop cluster. The proposed work integrates two text pattern matching algorithms with the map-reduce programming model for faster detection of malware in big data. The results of our study indicate that use of GPU decreases the processing time of text pattern matching algorithms in big data hadoop.