Distributed Computing and Internet Technology

This research addresses a novel distributed algorithm based on determination of scalar leaders for camera sensor activation so as to attain enhanced coverage of the geographic event region under consideration. The selection of scalar leaders is accomplished so that the leaders are organized in a hierarchical pattern with each of the child placed at least at a distance of twice of depth of field of camera nodes. Such distance is chosen so as to avert the possible overlapping among the field of views of cameras. Further, the chosen leaders perform as the agent of scalars that impart the concerned event information to their respective cameras. Experiments have been carried out to test the veracity of our proposed scheme. The least camera actuation, enhanced coverage ratio, least redundancy ratio, lowered energy as well as power expenditure attained from the investigation justify the efficacy of the proposed scheme.

Gathering can be coined as one of the primary interaction parameters in systems of autonomous mobile agents or sensors, known as robots. These robots are identical and placed in the nodes of an unlabeled graph. They operate in wait-look-compute-move cycles. In one cycle, first the sensors of the robots are activated independent of each other (wait). Then a robot takes a snapshot of the current configuration (look), makes a decision to stay idle or to move to one of its adjacent nodes (compute), and in the latter case makes an instantaneous move to this neighbor (move). Then the robot again goes back to its initial phase (wait). Cycles are performed asynchronously for each robot. The robots are oblivious, i.e., they do not use any computed data from the previous cycle. The robots do not agree on a common coordinate system. They cannot differentiate between a node having single robot and a node having multiple robots, i.e., multiplicity of a node. The robots are not able to see all the nodes of the graph. They do not know the total number of robots in the system. In this paper, we have developed two algorithms to gather these robots at a single node (not known beforehand) of a Ring Graph and an infinite Grid, in finite time. To the best of our knowledge, this is one of the first reported results on gathering multiple robots under limited visibility in an infinite grid and a ring.

The huge increase in the usage of mobile devices has led to the need of sophisticated optimization techniques in order to minimize energy wastage. In this paper we analyze energy consumption of mobile devices during the exchange of data, while walking in a WiFi network area, in order to study the dynamic of the power absorption. This analysis can be used in particular to develop suitable optimizations in case of poor signal. The analysis is obtained as an instance of the Abstract Interpretation framework for semantics-based software verification, and the results are validated by a preliminary real-case experimental evaluation.

Research shows that with the unexpected emergence of religion and faith, identifying religious conflicts within society has become an important problem for the government and law enforcement agencies. Many social science researchers and domain experts conduct manual surveys on offline and online bases for finding such conflicts. On the other hand, it is seen that people use social media websites for sharing their religious opinions, sentiments and beliefs. We create a hypothesis that social media websites are a rich source of information for mining these beliefs and automatically identifying the religious conflicts among users which overcomes the gaps of offline studies. In this paper, we address the challenge of ambiguity and multilingual scripts in social media posts and distinguish them into various religious sentiments of users. In order to evaluate our hypothesis, we conduct our study on Tumblr- the second most popular online micro-blogging service. We create a dataset of all Tumblr posts (published since 2007) consisting of several tags commonly used in religion based posts and make it publicly available for benchmarking and comparison. We investigate the efficiency of natural language based features for identifying the Tumblr posts that discuss about a religion and belong to one of the nine categories of users’ sentiments. For example, disagreement, defensive, annoyed and disappointment. We manually analyze these posts and our result shows the proposed features are discriminatory and support our hypothesis. Furthermore, our results reveal that despite the subjectivity in Tumblr posts, it is technically challenging to mine the religious sentiments of bloggers.

Feature Selection is an important preprocessing step in any machine learning model construction. Rough Set based feature selection (Reduct) methods provide efficient selection of attributes for the model without loss of information. Quick Reduct Algorithm is a key Reduct computation approach in Complete Symbolic Decision Systems. Authors have earlier implemented a scalable approach for Quick Reduct Algorithm as In-place MapReduce based Quick Reduct Algorithm using Twister’s Iterative MapReduce Framework. Improved Quick Reduct Algorithm is a standalone extension to Quick Reduct Algorithm by incorporating Trivial Ambiguity Resolution and Positive Region Removal. This work develops design and implementation of distributed/parallel algorithm for Improved Quick Reduct Algorithm by incorporation of Trivial Ambiguity Resolution and Positive Region Removal in In-place MapReduce based Quick Reduct Algorithm. Experiments conducted on large benchmark decision systems have empirically established the significance of computational gain and scalability of proposed algorithm in comparison to earlier approaches in literature.

Analysis of geo-distributed Big Data has been recently gaining importance. This is addressed either by copying data to a single data centre, or by processing data locally at each datacentre and aggregating the outputs at a single datacentre. Both involve expensive data transfers over wide area networks (WAN). In this work, we analyzed different models proposed for distributed MapReduce in various papers and selected a feasible model to simulate Map Reduce across distributed data centers. We have designed an extension to CloudSim and CloudSimEx to support three methods of implementing geo-distributed MapReduce. A heuristic decision algorithm is devised based on input, intermediate, and output files sizes to select suitable execution path.

Optimizing the frequency of a homogeneous multi-core platform for a Real Time Operating System (RTOS) increases the power utilization factor. The level of consistency of RTOS is concerning the amount of time taken to complete the execution of the hard real time task. Multi-core platforms need special techniques for process management and power management. DPM is one of the techniques designed to achieve the power efficiency in which all the cores have to operate at the same speed simultaneously which leads to the lack of flexibility for power management. This article, proposes an algorithm, dynamic computational energy aware where computing time of a task is increased based on the requirement in order to achieve exceptional power efficiency by considering multi-low power states.

Data center placement and service placement within a data center are the two most fundamental problems concerning cloud network design. In an ideal world, data centers should be placed in such a manner that there should be no data loss or service disruption, the costs incurred are minimum possible amounts, latency is minimized and it operates in a “green” way, i.e., minimal CO₂ emissions and runs on green energy. The service/content within a data center must be placed at locations that minimize content loss in case of any disaster, minimum latency for various requests, and also optimize available resources at hand. In this paper, we propose a data center and service placement problem that is aware of both CO₂ emissions and content loss due to disaster(s). The objective is to minimize the total cost which includes operational and setup cost of data center, and penalty associated with CO₂ emissions and expected content loss. This problem is modeled as a multi-objective optimization problem. Results show that the total cost incurred tend to increase with the number of disaster events and the setup and operational expenditures tend to increase with the number of software components. Furthermore, results also show that, for a given number of software components, CO₂ penalty of the CO₂ aware placement is less compared to the CO₂ unaware placement.

Massive technological advancements has promoted rise in energy costs, cloud computing being one of the key contributors. The cloud datacenters consume huge amount of energy which lead to carbon emissions, detrimental for our environment. Design of resource scheduling, load balancing and migration schemes for virtual machines (VM) in the cloud environment is one of the ways by which energy consumption can be minimized. This work proposes a prediction based VM migration approach (PMM) and explores how the proposed approach can affect the total energy consumption of a datacenter; with the aim to make the technology more environment friendly. PMM has been designed keeping the concept of the well-known Markov chain model in mind. Substantial amount of simulation has been conducted in this work to conclude how a prediction based resource management technique can play vital role in influencing the energy consumption of a cloud datacenter, in comparison to the existing and popular minimization of migrations (MM) policy.

Spot instance is an attractive pricing option to the cloud service users. But to use the spot instances, the cloud service users (CSU) have tos select an appropriate bid. The total cost of using spot instances and time to complete the user’s job are both determined by the bid chosen. Thus, selection of appropriate bid for spot instances is a critical problem for the CSUs because of the unpredictable nature of spot prices. The problem is more challenging when there is a deadline constraint. The Dynamic Bidding Algorithm (DBA) was considered to be an effective approach in this field. In this paper, we show that by combining a greedy approach for bidding with effective use of checkpointing, the total cost of using spot instances can be reduced further without violating the deadline constraint. To measure the cost of our solutions and those given by DBA with the optimum we have formulated an IPP for the given problem. In formulating the IPP it is assumed that spot prices at all future times are known. Experimental results based on some actual spot and on-demand price data show that the cost of our solution is comparable to those given by IPP. It is also observed that the cost decreases as the deadline to complete the job is extended.

The energy efficiency of cloud computing has drawn gigantic attention due to the explosive growth of cloud services. Moreover, this growth extends the capacity of various resources of the datacenters. As a circumstance, the amount of carbon footprints generated from the datacenters is sharply increased. Therefore, the objective is to use the datacenter’s resources proficiently without compromising the user requirements such that energy consumption is minimized. The recent studies have shown that the user requirements are provided in the form of virtual machines (VMs) which are deployed in the physical machines (PMs) of the datacenters based on the resource utilization or decreasing order of the VM capacity. However, these studies have not considered the capacity of the user requests. In this paper, we propose a request-based VM placement (RVMP) algorithm by considering the capacity of the requests. The proposed algorithm assigns the user requests to the VMs and further assigns the used VMs to the PMs based on the capacity of the requests and VMs respectively. Our simulation results on five different datasets, which are generated using Monte Carlo method, show that RVMP improves performance in terms of the number of used VMs and PMs, average PM utilization and energy consumption of PMs compared to state-of-the-art algorithms.

Attribute-based encryption enables the users to get the data only if attributes of the users are satisfied with the access policy embedded in the ciphertext. Whenever it is used in a collaborative environment, user’s claim policy has to be verified before permitting them to update the data. In the existing system, it is verified by the computationally expensive attribute-based signature. We are proposing an efficient method to compute the signature of the encrypted message and claim policy, which enables claim policy verification and thereby allow the users to modify the data. In this system, public cloud server (PCS) permits the users to modify the outsourced data in the cloud after verifying the user’s claim policy signature. It does not disclose the claim policy to the cloud service provider, PCS, and unauthorized users. Data owner is able to verify the integrity of the outsourced data set to ensure that the data is always intact with him. He can also validate the updated data by incorporating the signature. The proposed scheme is privacy preserving and avoids insider attack.

Security protocols are essential for establishing trust in electronic transactions over open networks. Currently used languages/logics for protocol specifications do not facilitate/force the designer to make explicit goals, intentional assumptions or the preceding history across interactions among the stakeholders. This has resulted in gaps in specifications which in turn have led to problems such as: (i) inefficient/non-optimal protocol designs, (ii) incompatible theoretical attacks discovered by analyzers due to different threat models and (iii) faulty or insecure implementations due to insufficient guidelines for the implementer. We have recently developed the readers-writers flow model (RWFM) that has several benefits, including simple and intuitive labels. In this paper, we demonstrate that the problem of incomplete protocol specification can be overcome by enriching them with labels from RWFM, which make explicit the assumptions and goals at each stage of the protocol. In particular, we use readers and writers as labels for data objects and roles for tracking information flows in a protocol that makes explicit the construction of new messages from components of previous messages and also the knowledge of roles at various stages. We illustrate our approach and demonstrate its advantages in comparison to prominent specification languages in the literature by using the example of Needham-Schroeder public key protocol. Further, we argue how the proposed approach leads to a robust protocol specification language including security/cryptographic protocols that shall be of immense aid to the designer, user and the implementer of protocols.

We present an efficient image authentication scheme using Genetic algorithm (GA). Using the crossover and mutation process of GA the original image is randomized into a binary string. A pairing function is then used as a checksum function that converts the binary string to a fixed length digest of the original image. A random permutation is used as the secret parameter between the authenticator generator and verifier. The scheme provides a non-reversible compression and collision resistance property, and is secure against chosen plaintext attacks. The experimental results show that the proposed scheme is efficient in comparisons to standard cryptographic authentication algorithms.

Signcryption is a public-key cryptographic primitive that is a synthesis of encryption and digital signature schemes. We present a signcryption scheme using anonymous attribute based encryption. The scheme uses the notion of identity-based digital signature on the message encrypted under the attribute based encryption scheme. The scheme provides both sender and receiver anonymity, in particular, only the legitimate receiver can identify the sender after the successful decryption operation. We show that the scheme is secure against adaptive chosen ciphertext attack and chosen message attack.

Over the years, internet has become the major source of security threat to computer systems. With the number of people browsing internet increasing exponentially in the last couple of years, browser based attacks have become the preferred means of infecting a computer system. These browser based attacks, known as ‘Drive-by Download’ attacks, inject malicious JavaScript from the server hosting the malicious web application to the browser. Since, the numbers of malicious websites launching such attacks have increased in the past few years; it has become critical to detect them. Typically, search for malicious web pages involves three steps- crawling URLs on the internet, using fast analysis filters to reject benign pages, and then running complex but slow detailed analysis (using Honey Clients) on the filtered list. While effective, these techniques consume substantial time and computing resources. This limitation can be overcome by designing a crawler which can seek more malicious sites than benign sites, thus, increasing the “toxicity” of the URLs collected in the first step. In this paper, we propose a focused web crawler, named “MalCrawler”, which has been designed to crawl and search malicious websites efficiently. This crawler, when compared to a generic crawler, will not only seek more malicious sites than benign sites, but will also handle cloaking, entanglement and AJAX content in malicious sites. MalCrawler, designed, developed and tested, as part of the scope of this paper, proved to be more efficient than generic crawlers.

According to the Varanasi edition of The Times of India, dated July 3, 2013, only 2% of the lost or stolen mobile phones in India are recovered [1]. This motivated the authors of this scholarly piece to work in this field and come up with Spying Mobrob – a solution that can improve the recovery success rate of stolen mobile phones. Spying Mobrob does not only help to recover stolen mobile phones but also to capture their thieves. In the present scenario, whenever a mobile phone is stolen, Spying Mobrob tries to track down the mobile number of its thief by dint of signaling data from mobile network operators. The innovation here is that Spying Mobrob does not trace the stolen mobile phone. Rather it tracks down the mobile phone of the thief and traces down the network of thieves involved in the process. For example, if X’s phone is stolen and Y is the thief, then Y’s phone will be tracked rather than X’s phone.

Clustering of large volumes of data is a complex problem which requires use of sophisticated algorithms as well as High Performance Computing hardware like a cluster of computers. It is highly desirable that data mining experts have a solution which on one hand provides a simple interface for ex-pressing their algorithms in terms of domain specific idioms and on the other hand automatically generates parallel code that can run on a cluster of multicore nodes. The proposed Domain Specific Language (DSL) along with its parallelizing compiler attempts to provide a solution. In this paper, we give the design of the DSL, called DWARF. Various language constructs have been described along with the rationale behind their inclusion in the language. A qualitative comparison of abstraction provided by DWARF is compared with MapReduce, Spark, and other MPI-based implementations to establish the usefulness of the proposed clustering DSL.

Data collection from distributed cloud storage through SaaS application requires efficient execution of the overall process. This type of cloud computing mechanism can be furnished through shared resources, improved service levels and minimal management effort. Data retrieval from cloud storage by different users through shared resources environment may blend data from different data requests in a single place. Here lies the need of efficient data retrieval strategy among different users that will help in secure data retrieval. When sensitive information is transmitted over the Internet there is high chance of information leakage. So, to prevent security attack like interference attack during query execution becomes a big challenge. This work explores a novel data retrieval strategy in extremely robust and linearly scalable high performance NoSQL database like Cassandra and presents a brief security analysis while retrieving stored data in shared environment.