Mourad Hakem

Dependability of wireless sensor networks for industrial prognostics and health management

HighlightsState of the art in prognostics and health management.Limitations of prognostic models.Dependability of wireless sensor networks.Challenges of remaining useful life prediction. Maintenance is an important activity in industry. It is performed either to revive a machine/component or to prevent it from breaking down. Different strategies have evolved through time, bringing maintenance to its current state: condition-based and predictive maintenances. This evolution was due to the increasing demand of reliability in industry. The key process of condition-based and predictive maintenances is prognostics and health management, and it is a tool to predict the remaining useful life of engineering assets. Nowadays, plants are required to avoid shutdowns while offering safety and reliability. Nevertheless, planning a maintenance activity requires accurate information about the system/component health state. Such information is usually gathered by means of independent sensor nodes. In this study, we consider the case where the nodes are interconnected and form a wireless sensor network. As far as we know, no research work has considered such a case of study for prognostics. Regarding the importance of data accuracy, a good prognostics requires reliable sources of information. This is why, in this paper, we will first discuss the dependability of wireless sensor networks, and then present a state of the art in prognostic and health management activities.

Efficient distributed lifetime optimization algorithm for sensor networks

One of the main design challenges in Wireless Sensor Networks (WSN) is to prolong the system lifetime, while achieving acceptable quality of service for applications. In WSN, each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. Due to the limited capabilities of sensor nodes, it is usually desirable that a WSN should be deployed with high density and thus redundancy can be exploited to increase the networks lifetime. In this paper, we introduce an efficient lifetime optimization and self-stabilizing algorithm to enhance the lifetime of wireless sensor networks especially when the reliabilities of sensor nodes are expected to decrease due to use and wear-out effects. Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed. We provide some theoretical and simulation results, that fully demonstrate the usefulness of the proposed algorithm.

Epidemiological approach for data survivability in unattended wireless sensor networks

Unattended wireless sensor networks (UWSNs) are wireless sensor networks characterized by sporadic sink presence and operation in hostile settings. The absence of the sink for period of time prevents sensor nodes to offload data in real time and offers greatly increased opportunities for attacks resulting in erasure, modification, or disclosure of sensor-collected data. In this paper, we focus on UWSNs where sensor nodes collect and store data locally and try to upload all the information once the sink becomes available. One of the most relevant issues pertaining UWSNs is to guarantee a certain level of information survivability in an unreliable network and even in the presence of a powerful attackers. In this paper, we first introduce an epidemic-domain inspired approach to model the information survivability in UWSN. Next, we derive a fully distributed algorithm that supports these models and give the correctness proofs.

Stabilization and Lifetime Optimization in Distributed Sensor Networks

One of the main design challenges in Wireless Sensor Networks (WSN) is to prolong the system lifetime, while achieving acceptable quality of service for applications. In WSN, each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. Due to the limited capabilities of sensor nodes, it is usually desirable that a WSN should be deployed with high density and thus redundancy can be exploited to increase the networks lifetime. In this paper, we introduce an efficient lifetime optimization and self-stabilizing algorithm to enhance the lifetime of wireless sensor networks especially when the reliabilities of sensor nodes are expected to decrease due to use and wear-out effects. Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed. We provide some theoretical and simulation results, that fully demonstrate the usefulness of the proposed algorithm.

Distributed Lifetime Optimization in Wireless Sensor Networks

Lifetime optimization becomes a commonplace feature of wireless sensor networks. In addition, as the scale is expanding, node reliabilities gradually become dynamically heterogeneous over time even if sensor nodes are symmetric by design. At the same time, as sensor nodes get smaller and approach technological limits, they suffer from increased susceptibility to wear-out which becomes a real problem as complex sensor networks with many nodes operate in unsafe and harsh environments for long times. Consequently, there is an increasing need for developing techniques to achieve more reliability, \textit{i.e.}, increase the networks operational time. In this paper, we introduce an efficient distributed failure-aware strategy (on-line solution) using probabilistic Weibull distribution to resist to frequent and unexpected fail-silent/fail-stop node failures. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed solution.

Resiliency in Distributed Sensor Networks for Prognostics and Health Management of the Monitoring Targets

In condition-based maintenance, real-time observations are crucial for on-line health assessment. When the monitoring system is a wireless sensor network (WSN), data loss becomes highly probable and this affects the quality of the remaining useful life prediction. In this paper, we present a fully distributed algorithm that ensures fault tolerance and recovers data loss in WSNs. We first theoretically analyze the algorithm and give correctness proofs, then provide simulation results and show that the algorithm is (i) able to ensure data recovery with a low failure rate and (ii) preserves the overall energy for dense networks.

Using an Epidemiological Approach to Maximize Data Survival in the Internet of Things

The Internet of Things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such a large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this article, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.

A New Reliable and Self-Stabilizing Data Fusion Scheme in Unsafe Wireless Sensor Networks

In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. Hence, we introduce first an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. Next, we present a fault tolerant scheme to resist to frequent and unexpected not concomitant fail-silent/fail-stop node failures. The major contribution of this paper is the design of an analytical expression (an upper bound) of the actual number of moves/iterations required by the algorithm. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

Self-stabilizing consensus average algorithm in distributed sensor networks

One important issue in sensor networks that has received renewed interest recently is average consensus, i.e., computing the average of n sensor measurements, where nodes iteratively exchange data with their neighbors and update their own data accordingly until reaching convergence to the right parameters estimate. In this paper, we introduce an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. We prove that the convergence of the fusion process is finite and express an upper bound of the actual number of moves/iterations required by the algorithm. This means that our algorithm is guaranteed to reach a stable situation where no load will be sent from one sensor node to another. We also prove that the load difference between any two sensor nodes in the network is within

Reliable distributed data fusion scheme in unsafe sensor networks

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