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Air pollution has become a major environmental risk of the new civilized world due to its severe influence on public health and the environment. Eventually, understanding the spatiotemporal variability of air pollution at high granularity is necessary to make relevant public policies. To explore spatiotemporal variability of air pollution at high granularity we have utilized the power of IoT based participatory sensing and data science. In this paper, we propose a predictive model for spatiotemporal air pollution estimation technique called Multiview data Fusion model (MVDF) that can consider spatial as well as temporal dependencies of air pollutants. The proposed technique is evaluated based on real-world air pollution dataset collected by participants over a period of 1 year in an urban area of city Kolkata. The results show that MVDF dominates over some baselines like Simple Kriging (SK), Modified Shepard’s Method (MSM) and Nearest Neighbor (NN). Besides, in this paper, we attempt to perform visual analysis that consists of state-of-the-art visualization techniques to explore spatiotemporal variability at different granularities on the estimated pollution levels of MVDF.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users’ identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.

Emotion detection is a promising field of research in multiple perspectives such as psychology, marketing, network analysis and so on. Multiple models have been suggested over the years for accurate and efficient mood detection. Identifying emotion, or mood, from text has progressed from a simple frequency distribution analysis to far more complicated learning approaches. The main aim of all these text mining and analysis is twofold. First is to categorise existing text into broad classes of emotions, such as happy, sad, angry, surprised and so on. The second aim is to accurately predict the moods of real-time streaming text. The novelty of the work lies in the extensive comparison of nine conventional learning methods with respect to performance metrics precision, recall, F1 and accuracy as well as studying the variance of mood over time using a wide array of moods (25). Using conventional classifiers allow near real-time predictions, can work on considerably less training data, and has the flexibility of feature engineering, as deep learning methods have feature engineering embedded in the model. Since a single line of text can be associated with multiple emotions, this article compares the performance of classifiers in predicting multiple moods for streaming text with likelihood-based ranking. An android application named Citizens' Sense was developed for text collection and analysis. The performance of mood classifiers are tested further using Twitter data related to COVID19. Based on the precision, recall, F1 and accuracy of the classifiers, it can be seen that Random Forest, Decision Tree and Complement Naive Bayes classifiers are marginally better than the other classifiers. The variance of mood over time, and predicted moods for text support this finding.

Forecasting time series has acquired immense research importance and has vast applications in the area of air pollution monitoring. This work attempts to investigate the abilities of various existing techniques when applied for short term, high granular time series forecasting of PM2.5. More specifically, a comparative study has been provided, taking into account both popularly used models and lesser-used models in this area. The study has been carried out considering ten well defined models that are ARIMA (auto-regressive integrated moving average), SARIMA (seasonal ARIMA), SES (single exponential smoothing), DES (double exponential smoothing), TES (triple exponential smoothing), ANN (artificial neural network), DT (decision tree), kNN (k-nearest neighbor), LSTM (long short-term memory) and MCFO (markov chain first order). A framework has been built that categories the models, implements them under identical execution environment and forecasts succeeding values. Implementation has been carried out over five data sets of real-world air pollution time series, that are collected from five differently located government setup monitoring stations over a period of 1 year (July 2018-June 2019). Rigorous statistical analysis has been performed that yields an insight to the nature and variability of these time series data. Forecasting has been carried out on short term basis, focusing on high granularity whereas, three different lengths of forecast horizon (1 day, 1 week, and 1 month) have been tested. Eventually, the models have been compared in terms of their associated performance measuring units namely, RMSE (root mean of squared error), MAE (mean absolute error) and MAPE (mean absolute percentage error). The comparative results verified with multiple datasets show that all the models posses less error for a shorter forecast horizon, where LSTM providing the best performance. Superiority of machine learning and deep learning models are found in case of longer length of forecast horizon with kNN achieving best accuracy whereas, significant performance degradation of ARIMA is found for longer forecast horizon. Moreover, TES, DT, kNN, LSTM, MCFO are found to be well adopted in relation with shape and variability of the data. Note that the performance on various length of high granular forecast horizon have been studied over multiple datasets that give an added value to this work.

The demand of cloud-based services is growing rapidly due to the high scalability and cost-effective nature of cloud infrastructure. As a result, the size of the data center is increasing drastically, so is the cost of maintenance in terms of resource management and energy consumption. Hence, it is important to develop a proper resource management plan to maximize the profit by reducing the overhead of operational cost. In this paper, we propose a multi-step-ahead workload prediction approach using Machine learning techniques and allocate the resources based on this prediction in a way that allows the resources to be utilized more efficiently and thereby, reducing the data center’s overall energy consumption. We evaluate the effectiveness of our framework based on real workload trace of Bitbrains. Experimental results show that our framework outperforms other state-of-the-art approaches for predicting workload over a long-run and significantly improves resource utilization while enabling substantial energy savings.

Indoor air pollutants e.g., Carbon dioxide (CO2), Particulate Matter(PM)2.5, PM10, Total Volatile Organic Compounds (TVOC), etc. have a serious impact on human health. Out of these pollutants, CO2 is one of the most dominant one. Hence, proper monitoring and control of this pollutant is an important part of maintaining a healthy indoor. To make this happen, it is required to predict the next moment’s indoor pollutant level at an acceptable accuracy range that ensures necessary steps can be taken beforehand to avoid a rise in the indoor pollution level for maintaining a healthy indoor all the time. It also helps people plan ahead, decreases the adverse effects on health and the costs associated. For this experiment, we have collected three months of real-life time-series data along with proper context information and have gone through feature engineering and feature selection process to create model ready data. Now, since the indoor CO2 concentration is dependent on multiple external factors (context data) which in turn is dependent on time, makes it a time-dependent function. Hence, to predict the indoor pollutant CO2, here we have used the time series forecasting model based on our collected data nature. This is a powerful tool and used in a wide range of research domains for predicting the next moment’s target value. This model ready data is utilized in forecasting different time series models. According to our findings, among the selected popular time series models, the SARIMAX time series model is best suited for this forecasting problem which is utilizing indoor context information along with historical data (with 10 Fold Time-Series Split Cross-Validation score 0.907). We have achieved an average of RMSE 26.45 ppm (i.e., 97.36% accuracy) level based on a three day average for indoor pollutant prediction which is outperforming other relevant models in this domain.

Energy harvesting facilitates Wireless Sensor Networks (WSN) to work in perpetual mode. But, the amount and duration of green energy depend on the unpredictable behavior of ambient energy sources. Limited knowledge of future energy availability is the main constraint in designing routing and MAC (Medium Access Control) protocol. If the prediction of future energy availability can be done with fine accuracy, then energy management strategies can be designed accordingly. Predicting solar irradiance value is a challenging issue as it has seasonal and daily temperature variation. Enhanced-Pro, a novel energy prediction strategy is proposed here by considering past energy observations as well as current energy intake, and its effectiveness is also tested. It is a modification of Pro-Energy, which is a landmark solar energy prediction solution in terms of accuracy. In this work, real-life solar traces are used for prediction. The algorithm incorporates two factors tuning factor and fine adjustment index. These two factors help to enhance prediction accuracy. Enhanced-Pro is compared with Pro-Energy, I-Pro-Energy, and QL-SEP. The simulation result proves Enhanced-Pro delivers better performance in the scale of computational complexity and accuracy.

Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.

In mobile crowd-sensing, smartphone users take part in sensing and then share the data to the server (cloud) and get an incentive. These data can be utilized for providing better services to improve quality of life. Batteries used in smartphones constrain the usability of these devices for longer charge cycles. Hence, maintaining a balance between energy consumption due to crowd-sensing application and that due to the current computational load on the device is the need of the hour. Consequently, in this paper, we formulate strategies applying Markov Decision Process (MDP) by which a smart handheld would crowd-sense while keeping the device active for a longer period of time. MDP used here helps to decide when a device would lend itself to crowd-sense considering the remaining energy of the device, it’s recharging probability, current computational load,and the incentive it receives. In this work, we have considered indoor localization as an example of a smartphone based crowd sensing application. The strategies found by solving MDP formulation are implemented for a smartphone application for crowd-sensed indoor localization. We have experimented using 5 smart handheld devices for different use cases. Our scheme is found to perform better than the state-of-the-art works.

Computation offloading effectively expands the usability of mobile terminals beyond their physical limits, and also greatly extends their battery charging intervals. Offloading or cyber foraging is a technique by which large and complex computational jobs are relocated from lightweight portable devices (such as smartphones) called offloadee to powerful servers (such as nearby laptops/desktops or cloud server over the Internet) called surrogates, and getting the output back at the offloadee. Many research works have been done so far on the architecture of offloading systems, but only few works can be found on fault detection and tolerance. So this paper concentrates on categorizing different failures that may affect the benefit of offloading computation and proposes a fault-tolerance approach to alleviate those faults. The checkpoint based fault-tolerance approach proposed in this paper is able to handle crash, omission and transient failures altogether. Fault prevention is obtained by utilizing historical data for choosing worthy surrogate from known neighborhood. This fault tolerance model is evaluated by applying a real application named $$\pi$$calculator and Scimark benchmark suite. The system is implemented and the proposed approach is found to be effective with respect to energy consumption and resource utilization even in case of crash and omission failure.

Changes in the BAN topology are caused due to the body movement induced by repeated activities like walking, running, twisting, turning and waving arms. In such activities, individual nodes may move relative to each other and along with this, the entire BAN may move its absolute location, which can induce several complexities in the network like re-transmission, collision, overhearing and changing interference. This paper presents a scheme referred as, posture based data transmission (PBDT) with the objective of efficient data transmission. PBDT is based on the occurrence of potential (best) posture over time in repeated activities. In PBDT, each node follows the procedures: (a) recognized the sequence of postures by observing the variation of received signal strength indicator (RSSI) from neighbor nodes over time, (b) finds the best posture from posture sequence for data transmission, (c) maintains a dynamic active/sleep schedule in order to reduce lossy transmission, collision and overhearing. Here, we consider walking as a repeated action over time to check the validity of the proposed mechanism. PBDT is implemented using Castalia simulator and compared with selected MAC protocols. The results are analyzed in respect to the energy consumption and throughput.

Proper maintenance of roads is an extremely complex task and also an important issue all over the world. One of the most critical road monitoring and maintenance activities is the detection of road anomalies such as potholes. Identification of potholes is necessary to avoid road accidents, prevent damage of vehicles, enhance travelling comforts, etc. Although maintenance of roads is considered to be a serious issue by the authorities over the years, lack of proper detection and mapping of road potholes makes the issue more severe. To overcome this problem, an end-to-end system called PotSpot is built for real-time detection, monitoring, and spatial mapping of potholes across the city A Convolutional Neural Network (CNN) model is proposed and evaluated on real-world dataset for pothole detection. Additionally, real-time pothole-marked maps are generated with the help of Google Maps API (Application Programming Interface). To provide an end-to-end service through this system, both the pothole detection and pothole mapping are integrated through an android application. The proposed model is also compared with six baselines namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and three pre-trained CNN models InceptionV3, VGG19 and VGG16 in terms of performance metrics to verify its effectiveness. The proposed model achieves better accuracy (≈ 97.6 %) as compared to the above-mentioned baseline methods. It is also observed that the Area Under the Curve (AUC) value for the proposed pothole detection model (AUC= 0.97) is higher than the baseline methods.

The technology, cloud computing, in present days, is vastly used due to the services it provides and the ease with which they can be availed. The enormous development of the Internet technology is due to the advent of the concept of cloud. Along with its benefits, cloud computing brings along itself a detrimental side effect, i.e., carbon emission. This is due to the massive energy consumption in the cloud data centers. Reduction in energy consumption in cloud is thus one of the major challenges among the researchers. This work conducts a thorough study of the various techniques that help in minimization of energy consumption in data centers. It also explores and proposes approaches to reduce the same, eventually making the environment greener. In the proposed work, prediction mechanism has been adopted and implemented on the existing Minimization of Migration (MM) policy for large history data set, followed by dynamic thresholding mechanism in place of static thresholds. Rigorous simulations have been conducted, and the results show reduction in cloud data center energy consumption.

The emergence of cloud computing has led to an astronomical growth in the computing services provided by vendors over the cloud interface. This has led to the paradigm of cloud federations where a group of CSPs collaborate to form a federation for seamless provisioning of resource requests. In this paper, cloud federation formation framework is modeled as a multi-objective optimization problem with the trade-off between profit and QoS. Federation formation algorithms try to maximize the federation profit while maintaining a balance between the QoS and the profit of the members of the federation. We have applied Linear Scalarization as well as $$\varepsilon $$ -constraint method to find the pareto-optimal solution to this multi-objective optimization problem. A heuristic-based algorithm for cloud federation formation following the integer linear program is proposed. We perform extensive experiments to investigate the performance of our proposed mechanism and show that our proposed mechanism yields optimized solution to the general problem of profit/QoS trade-off.

Cloud federation has paved the way for cloud service providers (CSP) to collaborate with other CSPs to serve users’ resource requests, which are prohibitively high for any single CSP during peak time. Moreover, to entice different CSPs to participate in federation, it is necessary to maximize the profit of all CSPs involved in the federation. Further, federation enables overloaded CSPs to distribute their load among other underloaded member CSPs of federation by migrating the virtual machines (VM). Migration of VM among member CSPs of federation, also enables to increase the reliability and availability of cloud services on occurrence of faults in the datacenters of CSPs. Thus it becomes important for CSPs to form a federation with other CSPs, in such a way that the migration cost of VMs between CSPs of the same federation is minimized and simultaneously profit of CSPs in federation is maximized. In this paper, we model the problem of forming federation among CSPs as a hedonic coalition game, with a utility function depending on profit and migration cost, with the objective of maximizing the former and minimizing the latter. We propose an algorithm to solve this hedonic game and compare its performance with other existing game-theory based cloud federation formation mechanisms.

By embracing the potential of IoT and smartphones, traditional cities can be transformed to smart cities. The success of such smart city mission is firmly vested in populace and thus it should have a bottom-up nature, initiated by the citizens. This paper focuses on the design and development of a unified framework, which can provide a platform to empower all the applications across different dimensions of urban life in a smart city. The aim of this framework is to connect citizens, data, knowledge and services related to IoT as well as smartphone based applications. Here, we categorize all the applications for the smart city in three representative types, viz. IoT based, IoT and smartphone based and smartphone as IoT based applications. We have also developed and tested one prototype following this architecture for each of these three representative category type, i.e, IoT based smart classroom, IoT and smartphone based air quality monitoring system and only smartphone based noise monitoring system to demonstrate the effectiveness of the proposed framework for the smart city scenario.

Information propagation in the network is probabilistic in nature; simultaneously, it depends on the connecting paths of the propagation. Selection of seed nodes plays an important role in determining the levels and depth of the contagion in the network. This paper presents a comparative study when seed nodes for information propagation are selected through the properties of different centrality measures in the social network. This study captures the interaction measures of nodes in the social network, selects seed nodes based on five centrality measures, i.e. degree distribution, betweenness centrality, closeness centrality, Eigenvector and PageRank, and compares the affected nodes and levels of propagation within the network. We demonstrate the performance of the different centrality measures by processing three datasets of social network: Twitter network, Bitcoin network and author collaborative network. For the propagation of the information, we use breadth-first search (BFS) and susceptible–infectious–recovered (SIR) model and a detailed comparative study is also presented for each of the seed nodes selected using aforementioned network properties. Results show that the Eigenvector centrality and PageRank centrality measures outperform other centrality measures in all test cases in terms of propagation level and affected nodes during information propagation. Both Eigenvector and PageRank network data processing required a high computational overhead. For this reason we propose a hybrid model where using k-core the network is degenerated into a smaller network and centrality nodes are extracted from the smaller network. These centrality nodes, as compared to original centrality nodes, perform almost in the same manner in terms of influence maximization when k is chosen in a rational way.

Background India faces a high burden of child undernutrition. We evaluated the effects of two community strategies to reduce undernutrition among children under 3 years in rural Jharkhand and Odisha, eastern India: (1) monthly Participatory Learning and Action (PLA) meetings with women’s groups followed by home visits; (2) crèches for children aged 6 months to 3 years combined with monthly PLA meetings and home visits. Methods We tested these strategies in a non-randomised, controlled study with baseline and endline cross-sectional surveys. We purposively selected five blocks of Jharkhand and Odisha, and divided each block into three areas. Area 1 served as control. In Area 2, trained local female workers facilitated PLA meetings and offered counselling to mothers of children under three at home. In Area 3, workers facilitated PLA meetings, did home visits, and crèches with food and growth monitoring were opened for children aged 6 months to 3 years. We did a census across all study areas and randomly sampled 4668 children under three and their mothers for interview and anthropometry at baseline and endline. The evaluation’s primary outcome was wasting among children under three in areas 2 and 3 compared with area 1, adjusted for baseline differences between areas. Other outcomes included underweight, stunting, preventive and care-seeking practices for children. Results We interviewed 83% (3868/4668) of mothers of children under three sampled at baseline, and 76% (3563/4668) at endline. In area 2 (PLA and home visits), wasting among children under three was reduced by 34% (adjusted Odds Ratio [aOR]: 0.66, 95%: 0.51–0.88) and underweight by 25% (aOR: 0.75, 95% CI: 0.59–0.95), with no change in stunting (aOR: 1.23, 95% CI: 0.96–1.57). In area 3, (PLA, home visits, crèches), wasting was reduced by 27% (aOR: 0.73, 95% CI: 0.55–0.97), underweight by 40% (aOR: 0.60, 95% CI: 0.47–0.75), and stunting by 27% (aOR: 0.73, 95% CI: 0.57–0.93). Conclusions Crèches, PLA meetings and home visits reduced undernutrition among children under three in rural eastern India. These interventions could be scaled up through government plans to strengthen home visits and community mobilisation with Accredited Social Health Activists, and through efforts to promote crèches. Trial registration The evaluation was registered retrospectively with Current Controlled Trials as ISCRTN89911047 on 30/01/2019. Electronic supplementary material The online version of this article (10.1186/s12889-019-7274-3) contains supplementary material, which is available to authorized users.

Energy efficiency is one of the key challenges for designing routing algorithms for wireless sensor networks (WSN). Fast data delivery from source node to sink node is also required for many applications. Sometimes data transmission towards the sink may be interrupted because of failure of nodes in a particular area. The various such requirements of routing data in a WSN are sometimes contradictory and routing algorithms must be designed keeping in mind all these requirements. In this paper three routing algorithms are proposed for multi-sink partitioned network. In these three algorithms, the source nodes or intermediate nodes select a next node to forward the data to the destination or sink. This process repeats until the data reaches the sink. In the first technique, the next node is chosen considering its distance from sink node as a criterion. In the second technique, remaining energy of the neighboring nodes is used as a criterion for selection of the next node. The combination of these two criteria is considered in the third technique and a multiple criteria decision analysis technique is used. A recovery technique from area failure is also proposed and incorporated in the above mentioned algorithms. The algorithms are designed and implemented in multi-sink environment for partitioned wireless sensor networks. Performances of the algorithms are studied in a simulation environment and compared with other well-known algorithms to understand their effectiveness.

The nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) plays a central role in regulating metabolism, including interaction with the estrogen receptor-α (ERα). Significantly, PPARγ activity can be modulated by small molecules to control cancer both in vitro and in vivo (Yin et al., Cancer Res 69:687-694, 2009). Here, we evaluated the effects of the PPARγ agonist GW7845 and the PPARγ antagonist GW9662 on DMBA-induced mammary alveolar lesions (MAL) in a mouse mammary organ culture. The results were as follows: (a) the incidence of MAL development was significantly inhibited by GW 7845 and GW 9662; (b) GW9662 but not GW7845, in the presence of estradiol, induced ER and PR expression in mammary glands and functional ERα in MAL; (c) while GW9662 inhibited expression of adipsin and ap2, GW 7845 enhanced expression of these PPARγ-response genes; and (d) Tamoxifen caused significant inhibition of GW9662 treated MAL, suggesting that GW9662 sensitizes MAL to antiestrogen treatment, presumably through rendering functional ERα and induction of PR. The induction of ERα by GW9662, including newer analogs, may permit use of anti-ER strategies to inhibit breast cancer in ER- patients.

In a computational grid, jobs must adapt to the dynamically changing heterogeneous environment with an objective of maintaining the quality of service. In order to enable adaptive execution of multiple jobs running concurrently in a computational grid, we propose an integrated performance-based resource management framework that is supported by a multi-agent system (MAS). The multi-agent system initially allocates the jobs onto different resource providers based on a resource selection algorithm. Later, during runtime, if performance of any job degrades or quality of service cannot be maintained for some reason (resource failure or overloading), the multi-agent system assists the job to adapt to the system. This paper focuses on a part of our framework in which adaptive execution facility is supported. Adaptive execution facility is availed by reallocation and local tuning of jobs. Mobile, as well as static agents are employed for this purpose. The paper provides a summary of the design and implementation and demonstrates the efficiency of the framework by conducting experiments on a local grid test bed.