Mustapha Reda Senouci

Localized Movement-Assisted SensorDeployment Algorithm for HoleDetection and Healing

One of the fundamental services provided by a wireless sensor network (WSN) is the monitoring of a specified region of interest (RoI). Considering the fact that emergence of holes in the RoI is unavoidable due to the inner nature of WSNs, random deployment, environmental factors, and external attacks, assuring that the RoI is completely and continuously covered is very important. This paper seeks to address the problem of hole detection and healing in mobile WSNs. We discuss the main drawbacks of existing solutions and we identify four key elements that are critical for ensuring effective coverage in mobile WSNs: 1) determining the boundary of the RoI, 2) detecting coverage holes and estimating their characteristics, 3) determining the best target locations to relocate mobile nodes to repair holes, and 4) dispatching mobile nodes to the target locations while minimizing the moving and messaging cost. We propose a lightweight and comprehensive solution, called holes detection and healing (HEAL), that addresses all of the aforementioned aspects. The computation complexity of HEAL is O(v2) , where v is the average number of 1-hop neighbors. HEAL is a distributed and localized algorithm that operates in two distinct phases. The first identifies the boundary nodes and discovers holes using a lightweight localized protocol over the Gabriel graph of the network. The second treats the hole healing, with novel concept, hole healing area. We propose a distributed virtual forces-based local healing approach where only the nodes located at an appropriate distance from the hole will be involved in the healing process. Through extensive simulations we show that HEAL deals with holes of various forms and sizes, and provides a cost-effective and an accurate solution for hole detection and healing.

An Evidence-Based Sensor Coverage Model

A key challenge in the success of wireless sensor networks deployment is to consider the imperfections associated with sensor readings. Existing sensor coverage models such as the binary model and the probabilistic model may not be realistic in many cases and remain limited. Based on the transferable belief model, this letter defines an evidence-based sensor coverage model that reflects reality well and can be easily extended to include deployment-related issues, such as sensor reliability. As an example of application, we devise an evidence-based detection coverage model. Experimental results based on both synthetic data sets and data traces collected in a real experiment for vehicle detection are provided to demonstrate the benefits of the evidence-based sensor coverage model over state-of-the-art coverage models.

Movement-Assisted Sensor Deployment Algorithms: A Survey and Taxonomy

One of the fundamental design issues in mobile wireless sensor networks is how to design efficient movement-assisted sensor deployment algorithms that relocate the sensor nodes in order to meet the desired performance goals. This survey focuses on a variety of movement-assisted sensor deployment algorithms that have been proposed and studied by researchers and highlights their strengths and limitations. The various models, assumptions, objectives, and constraints are identified, and the different formulations are enumerated. A taxonomy of movement-assisted sensor deployment algorithms that captures the fundamental differences among existing solutions is introduced. Six classes of approaches are identified, each one of them uses a specific principle to relocate the nodes from their initial position to a new target position. The proposed taxonomy is used to provide an exhaustive classification of existing approaches. For each identified class, various self-deployment algorithms are discussed. Furthermore, comparisons between the different algorithms and also between the different classes are performed, therefore providing not only a complete view of the state-of-the-art but also useful insights for selecting the self-deployment algorithm most appropriate to the application at hand. This paper also highlights open problems in this area of research.

Uncertainty-Aware Sensor Network Deployment

In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.

Optimizations and Performance Study of the Dynamic Source Routing Protocol

We have conducted a performance study of the DSR protocol, in particular, for scenarios where it presents poor performance compared to that of AODV protocol. We have also carried out two optimizations on DSR, namely: generalized salvaging mechanism and cache update of the intermediate nodes with source routes discovered during the route request packets propagation. Our routing protocol, called GSM-DSR (generalized salvaging mechanism for DSR), is compared with AODV and DSR, and performance results are presented in this paper. We show, through simulations, that the salvaging optimization and the cache update do lead to an improvement of DSR performance, particularly, in large scale networks with high mobility and heavy load.

Fortifying Intrusion Detection Systems in Dynamic Ad Hoc and Wireless Sensor Networks

We investigate three aspects of dynamicity in ad hoc and wireless sensor networks and their impact on the efficiency of intrusion detection systems (IDSs). The first aspect is magnitude dynamicity, in which the IDS has to efficiently determine whether the changes occurring in the network are due to malicious behaviors or or due to normal changing of user requirements. The second aspect is nature dynamicity that occurs when a malicious node is continuously switching its behavior between normal and anomalous to cause maximum network disruption without being detected by the IDS. The third aspect, named spatiotemporal dynamicity, happens when a malicious node moves out of the IDS range before the latter can make an observation about its behavior. The first aspect is solved by defining a normal profile based on the invariants derived from the normal node behavior. The second aspect is handled by proposing an adaptive reputation fading strategy that allows fast redemption and fast capture of malicious node. The third aspect is solved by estimating the link duration between two nodes in dynamic network topology, which allows choosing the appropriate monitoring period. We provide analytical studies and simulation experiments to demonstrate the efficiency of the proposed solutions.

Fusion-based surveillance WSN deployment using Dempster-Shafer theory

In mission-critical Wireless Sensor Networks surveillance applications, a high detection rate coupled with a low false alarm rate is essential. Additionally, fusion methods can be employed with the hope that aggregation of uncertain information from multiple sensors enhances the quality of surveillance provided by the network. This paper investigates the following fundamental problem: what is the best way to deploy a finite number of unreliable sensors characterized by uncertain readings in order to satisfy the user detection requirements. Unlike prior efforts that rely on simple fusion schemes, we use the Dempster-Shafer theory to define a generic evidence fusion scheme that captures several characteristics of real-world applications. The fusion-based uncertainty-aware sensor networks deployment problem is formulated as a binary non-linear and non-convex optimization problem that is NP-hard, and an efficient heuristic using genetic algorithms is investigated. The effectiveness and efficiency of the proposed approach are evaluated using both simulations and experiments. The obtained results demonstrate the appropriateness of the evidence fusion model that considers in a meaningful way the information on the quality of sensors decisions as well as the reliability of these sensors along with their uncertain and imprecise decisions. Also, the proposed approach outperforms state-of-the-art deployment strategies.

Belief Functions in Telecommunications and Network Technologies: An Overview

In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks, robotics, and finance. This is due to the fact that imperfect information permeates the real-world applications, and as a result, it must be incorporated into any information system that aims to provide a complete and accurate model of the real world. Although, it is in an early stage of development relative to classical probability theory, evidence theory has proved to be particularly useful to represent and reason with imperfect information in a wide range of real-world applications. In such cases, evidence theory provides a flexible framework for handling and mining uncertainty and imprecision as well as combining evidence obtained from multiple sources and modeling the conflict between them. The purpose of this paper is threefold. First, it introduces the basics of the belief functions theory with emphasis on the transferable belief model. Second, it provides a practical case study to show how the belief functions theory was used in a real network application, thereby providing guidelines for how the evidence theory may be used in telecommunications and networks. Lastly, it surveys and discusses a number of examples of applications of the evidence theory in telecommunications and network technologies.

An analysis of intrinsic properties of stochastic node placement in sensor networks

A Wireless Sensor Network (WSN) consists of many sensors that are densely deployed to monitor a field. The sensor-positions can be predetermined to guarantee the quality of surveillance provided by the WSN. In remote or hostile sensor field, randomized sensor placement often becomes the only option. In this paper, we survey existing stochastic node placement strategies. We categorize stochastic placement strategies into simple and compound. A simulation study has been carried out yielding a detailed analysis of random deployment intrinsic properties, such as coverage, connectivity, fault-tolerance and network lifespan. The obtained results can give helpful design guidelines in using stochastic deployment strategies, and allow engineers to choose the deployment strategy appropriate to the situation and the goals.

Using the Belief Functions Theory to Deploy Static Wireless Sensor Networks

The location of sensors is one of the fundamental design issues in wireless sensor networks. It may affect the fulfillment of the systems requirements and mul- tiple network performance metrics. Assuming that an inherent uncertainty can be associated with sensor readings, it is very important to consider this issue in the de- ployment process to anticipate this sensing behavior. This paper addresses the issue of uncertainty-aware sensor networks deployment by exploiting the belief functions reasoning framework. An evidence-based coverage model is proposed and some possible extensions are discussed. The deployment problem is formulated as an op- timization problem and possible solutions are discussed. Preliminary experimental analysis demonstrates very promising results of the proposed methodology.