Ado Adamou Abba Ari

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.

FDRA: Fault Detection and Recovery Algorithm for Wireless Sensor Networks

Failures are inevitable in wireless sensor network, and it is important to detect and recover faulty nodes. In this paper, we present an algorithm to recover faulty nodes called Fault detection and Recovery Algorithm (FDRA). The performance evaluation is tested through simulation to evaluate some factors such as: Packet delivery ratio, control overhead, memory overhead and fault recovery delay. We compared our results with referenced algorithm: Fault Detection in Wireless Sensor Networks (FDWSN), and found that our FDRA performance outperforms that of FDWSN.

A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks

Abstract Wireless sensor networks (WSNs) face many security issues. When external attacks can be prevented with traditional cryptographic mechanisms; internal attacks remain difficult to be eliminated. Trust and reputation have been recently suggested by many researches as a powerful tool for guaranteeing an effective security mechanism. They enable the detection and the isolation of both faulty and malicious nodes. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies especially in the case of dishonest recommendations attacks, i.e. badmouthing, ballot-stuffing and collusion attacks. In this paper, we propose a novel bio inspired trust model for WSNs namely Bee-Trust Scheme (BTS) based on the use of both a modified cloud model and a cognitive chronometry parameter. The objective of the scheme is to achieve both a higher detection rate and a lower false positive rate of dishonest recommendations attacks by allowing the distinction between erroneous recommendations and dishonest ones which has thus far been overlooked by most research work. Simulation results demonstrate that the proposed scheme is both effective and lightweight even when the number of dishonest recommenders is large.

Geo-spatial Domain Ontology: The Case of the Socio-Cultural Infrastructures

This paper presents a geo-spatial domain ontology (CriSO) modeling approach, which is based on the RCC-8 model complemented by directional relations encoded by cone-shaped or alternatively, projection-based relations. Ontologies can be defined as a kind of semantic networks for the the real world description, they are essentially graphs between concepts linked by relations such as is_a, has_a, part_whole. But the scope of geographic ontologies applied to sociocultural features requires to describe not only the geographic features, but also their spatial relationships. Usually, only topological relations are defined, but other spatial, geographic relations and cultural knowledge must be considered as well. Thus, CriSO allows to annotate, to organize data, to facilitate information retrieval by introducing a semantic layer in the on-based Knowledge Management Systems and to integrate the local knowledge in the cloud of the Linked Open Data.

Efficient and Scalable ACO-Based Task Scheduling for Green Cloud Computing Environment

Cloud Computing has emerged as a popular technology that support computing on demand services by allowing users to follow the pay-per-use-on-demand model. Minimizing energy consumption in cloud systems has many benefits that enable green computing. Energy aware task scheduling in cloud to the users by service cloud providers has non negligible influences on optimal resources utilization and thereby on the cost benefit. The traditional algorithms for task scheduling are not well enough for cloud computing. In such environment, tasks should be efficiently scheduled such a way that the makespan is reduced. In this paper, we proposed a biologically inspired scheduling scheme, which is a based on a modified version of the ant colony optimization that aims at reducing the makespan time while ensuring load balancing among resources in order to enable green computing. Experiments of the proposed scheme in various scenario have been conducted in order to elaborate the impact of proposed models in the reduction of makespan. The obtained results demonstrate the effectiveness of the proposal in regards to the compared algorithms.