Blaise Omer Yenke

A power efficient cluster-based routing algorithm for wireless sensor networks

The design of low-power scalable wireless sensor networks remains a key challenge of the research. Clustering and routing have been widely studied for extending the lifetime of a network, which is a critical issue in sensor networks. Routing involves non-negligible operations, which considerably affect the network lifetime and the throughput. The clustering technique with data aggregation on cluster heads has an influence on the overall performance of the network since it is favoring a maximum network lifetime. This paper presents a novel cluster-based routing protocol called ABC-SD. The proposed protocol exploits the biologically inspired fast and efficient searching features of the Artificial Bee Colony metaheuristic to build low-power clusters. For the choice of cluster heads, a multi-objective fitness function is designed by using a Linear Programming formulation. The routing problem is addressed by a cost-based function that makes a trade-off between the energy efficiency and the number of hops of the path. The clustering process is achieved at the Base Station with a centralized control algorithm, which exploits energy levels and the neighborhood information of location-unaware sensors. As for the routing of gathered data, it is realized in a distributed manner. Furthermore, unlike the existing protocols in the literature, a realistic energy model is adopted in the considered network model. The proposed protocol is intensively experimented with a number of topologies in various network scenarios and the results are compared with the well-known cluster-based routing protocols that include the swarm intelligence based protocols. The obtained results demonstrate the effectiveness of the proposed protocol in terms of network lifetime, network coverage and the amount of packets delivered to the Base Station. HighlightsLP formulation of the clustering problem.Routing problem addressed by a CF.ABC-based clustering algorithm with a tradeoff between the energy consumption and the quality of the communication link.Pre-established routing mechanism in which routing paths are less costly in terms of power consumption.Integration of a realistic energy model and realistic network settings and simulation of the proposed protocol to demonstrate its performance compared to some existing protocols.

CONCEPTS AND EVOLUTION OF RESEARCH IN THE FIELD OF WIRELESS SENSOR NETWORKS

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field.

Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks

Future generations of radio-based networks promise new timeliness for collaborative low-power sensing schemes in wireless sensor networks. Due to the hostile and inaccessible environment in which sensors are deployed, collect and transfer data in such networks is not an easy task. An effective data gathering can be improved by introducing unmanned aerial vehicles called drones, which act as mobile sinks and can autonomously fly over the network with the primary goal of collecting data from sensors. This paper presents a biologically inspired scheme of collaborative mobile sensing. The proposal has been designed in such a way that the coverage, the energy efficiency and a high network availability are maintained. Social foraging behaviors of the Escherichia coli bacteria modeled in the bacterial foraging optimization have been used to achieve these goals, especially the chemotaxis and the swarming features that allow bacteria to move. After a description, a formalization of the problem of mobile sensing is presented. Then, models that allow mobile sinks to move in a self-organized and self-adaptive way is proposed. In order to highlight the impact of mobility on energy consumption, delay, network coverage and successful amount of delivered data, intensive experiments have been done. Results demonstrate the effectiveness of the approach.

Energy efficient clustering algorithm for Wireless Sensor Networks using the ABC metaheuristic

Designing an energy-efficient and scalable sensor network while maximizing the lifetime remains a challenge. A number of protocols have been proposed in order to provide a good management in Wireless Sensor Networks (WSNs). Routing involves non-negligible operations that considerably affect the network lifetime and throughput. The clustering technique with data aggregation on cluster heads provides an efficient scalability in WSNs, favoring a better network lifetime. In this paper, we present an energy efficient biologically inspired clustering protocol that uses the efficient and fast searching features of Artificial Bee Colony (ABC) algorithm. In the proposed protocol, a centralized clustering process is adopted while the data gathering and routing operations are realized in a distributed manner. The proposed protocol is intensively experimented and the results are compared with the some of the well-known clustering and routing protocols. The obtained results demonstrate the effectiveness of the proposed protocol in terms of network lifetime and the amount of transferred packets.

Adaptive scheme for collaborative mobile sensing in wireless sensor networks: Bacterial foraging optimization approach

In Wireless Sensor Networks, mobile sensing refers to the presence of one or more mobile sinks or mobile sensors, which have the main role of collecting the gathered data by sensor nodes. This paper describes a new scheme of mobile sensing that aims at providing a good coverage and throughput while maintaining better energy efficiency and high network availability. To achieve this, some features of the social foraging behavior of the Escherichia coli bacteria have been used, especially the chemotaxis and swarming processes that allow bacteria to move. Particularly, a description and a formulation of a mobile sensing scheme based on an approach inspired by the Bacterial Foraging Optimization have been provided. Models that allow mobile sinks to move over the network in a self-organized and self-adaptive way have been proposed. The proposal has been experimented in order to elaborate the impact of mobility on delay, network coverage and successful amount of collected data. The obtained results demonstrate the effectiveness of the proposal.

Clustering algorithm for wireless sensor networks : the honeybee swarms nest-sites selection process based approach

A cluster-based sensor network with data aggregation on cluster heads is the most popular approach for optimising the energy consumption, in order to maximise the overall network lifetime. Clustering is also used for optimising the quality of service and scalability in sensor networks. In large-scale networks, the management of nodes becomes a challenge. It is therefore necessary that, these sensors act in a self-organised manner to perform tasks. A number of protocols has been proposed. Swarm intelligence based models, inspired by social insects behaviours, provide the most powerful tools that lead to a global intelligence through simple actions in a self-organised manner. In this paper, we proposed a distributed clustering approach called NEST, based on the nest-sites selection process of a honeybee. Extensive experiments have been conducted and the results demonstrated that our algorithm delivers better performance in terms of network lifetime, delivered packets, end-to-end delay, energy consumption and efficiency.

RESOURCE MANAGEMENT OF VIRTUAL INFRASTRUCTURE FOR ON-DEMAND SAAS SERVICES

With the emerging of cloud computing, offering software as a Service appears to be an opportunity for software vendors. Indeed, using an on-demand model of provisioning service can improve their competitiveness through an invoicing tailored to customer needs. Virtualization has greatly assisted the emerging of on-demand based cloud platforms. Up until now, despite the huge number of projects around cloud platforms such as Infrastructure-as-a-Service, less open research activities around SaaS platforms have been carried on. This is the reason why our contribution in this work is to design an open framework that enables the implementation of on-demand SaaS clouds over a high-performance computing cluster. We have first focused on the framework design and from that have proposed an architecture that relies on a virtual infrastructure manager named OpenNebula. OpenNebula permits to deal with virtual machines life-cycle management, and is especially useful on large scale infrastructures such as clusters and grids. The work being a part of an industrial project, we have then considered a case where the cluster is shared among several applications owned by distinct software providers. After studying in a previous work how to implement the sharing of an infrastructure in such a context, we now propose policies and algorithms for scheduling jobs. In order to evaluate the framework, we have evaluated a prototype experimentally simulating various workload scenarios. Results have shown its ability to achieve the expected goals, while being reliable, robust and efficient.

Scheduling of Computing Services on Intranet Networks

Nowadays, enterprises can provide computing services through their intranet networks by letting their available resources be used as virtual clusters for scientific computation during idle periods such as nights, weekends, and holidays. Generally, these idle periods do not permit to carry out the computations completely. It is therefore necessary to save the context of uncompleted applications for possible restart. This checkpointing mechanism is subject to resource constraints: the network bandwidth, the disk bandwidth, and the delay T imposed for releasing the workstations. We first introduce a function bw that gives the bandwidth bw(m,V) of a system during the checkpointing of m applications with aggregated memory requirement V. Assuming that this bandwidth is shared equitably among the applications, the scheduling problem becomes a sequence of knapsack problems with nonlinear constraints for which we propose approximate solutions. Experiments carried out on Grid5000 show that the running time of this algorithm is negligible compared to the delay T which is of the order of few minutes. This means that the proposed scheduling algorithm does not induce a significant overhead on the checkpointing process. As a consequence, our mechanism can be incorporated in a batch scheduler.

Scheduling Deadline-Constrained Checkpointing on Virtual Clusters

We consider a context where the available resources of the Intranet of a company are used as a virtual cluster for scientific computation, during the idle periods (nights, weekends, holidays, ). Generally, these idle periods do not permit to carry out completely the computations. For instance, a workstation mobilized during the night must be released in the morning to make it available for the employee, even if the application running on it is not completed. It is therefore necessary to save the context of uncompleted applications for possible restart. Hereafter, we assume that the computations running on the workstations are independent from each other. The checkpointing mechanism which ensures the continuity of applications is subject to resource constraints : the network bandwidth, the disk bandwidth and the delay T imposed for releasing the workstations. We first show that the designing of a scheduling strategy which optimizes resource consumption while taking into account the above constraints, can be formalized as a variant of the classical 0/1 knapsack problem. We then propose an algorithm whose implementation does not have a significant overhead on checkpointing mechanisms. Experiments carried out on a real cluster show that this algorithm performs better than the naive scheduling algorithm which selects the applications one after the other in order of decreasing amount of resource consumption.

A data analysis system to extend the coverage capacity of meteorological stations for flood forecasting

Forecast Data provided by meteorological stations (MS) are crucial for Flood Forecasting Systems (FFS). These data are mainly related to temperature and precipitation. However, having enough MS to produce paramount of such data is challenging due to the high cost of their set up as well as their maintenance. As a consequence, it is almost impossible to get flood predictions in some regions due to the lack of meteorological forecast data. One solution to overcome such a drawback is to envision extending the data validity of a given area to another one. That is, we aim at using a MS of region A for estimating data we may have in region B if ever it had its own MS. In this respect, we propose an extension of MS forecast capacity by introducing a data analysis system based on a linear correlation technique. The system uses data collected from sensors networks installed on a given area not covered by a MS with data from a reference area that has a MS. Afterwards, it checks whether there is a linear correlation between the data of the two zones. In the affirmative case, a correlation function is deduced between the two areas and will be used for estimating data of the area without a MS. The results obtained from empiric experiments show the feasibility of our approach and its benefits.