Hybrid Wireless Sensors Deployment Scheme with Connectivity and Coverage Maintaining in Wireless Sensor Networks

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

     

Road network layout based multi-hop broadcast protocols for Urban Vehicular Ad-hoc Networks

Wireless Networks - In recent years, the decentralized wireless Vehicular Ad hoc Networks (VANETs) have emerged as a key technology for Intelligent Transportation Systems (ITS). The need...     

A Risk-Aware Reputation-Based Trust Management in Wireless Sensor Networks

Wireless sensor networks (WSNs) are susceptible to many security threats and are specifically prone to physical node capture in which the adversary can easily launch the so-called insider attacks such as node compromise, bypassing the traditional security mechanisms based on cryptography primitives. So, the compromised nodes can be modified to misbehave and disrupt the entire network and can successfully perform the authentication process with their neighbors, which have no way to distinguish fraudulent nodes from trustworthy ones. Trust and reputation systems have been recently suggested as a powerful tools and an attractive complement to cryptography-based schemes in securing WSNs. They provide ability to detect and isolate both faulty and malicious nodes. Considerable research has been done on modeling and managing trust and reputation. However, trust topic issue in WSNs remains an open and challenging field. In this paper, we propose a Risk-aware Reputation-based Trust (RaRTrust) model for WSNs. Our novel framework uses both reputation and risk to evaluate trustworthiness of a sensor node. Risk evaluation is used to deal with the dramatic spoiling of nodes, which makes RaRTrust robust to on–off attack and differ from other trust models based only on reputation. This paper contributes to model the risk as opinion of short-term trustworthiness combining with traditional reputation evaluation to derive trustworthiness in WSNs.

     

Reactive and adaptive monitoring to secure aggregation in wireless sensor networks

Data aggregation is considered as one of the fundamental distributed data processing procedures for saving the energy and minimizing the medium access layer contention in wireless sensor networks. However, sensor networks are likely to be deployed in an untrusted environment, which make them vulnerable against several attacks. A compromised node may forge arbitrary aggregation value and mislead the base station into trusting a false reading. Secure in-network aggregation can detect such manipulation. But, as long as such subversive activity is, reliable aggregation result can not be obtained. In contrast, the collection of individual sensor node values is robust and solves the problem of availability, but in an inefficient way. Our work seeks to bridge this gap in secure data collection. We propose a framework that enhances availability with efficiency close to that of in-network aggregation avoiding over-reliance on sensors. To achieve this, we design a scheme that is built on one core concept: no trust is supposed in any sensor. Therefore, we design a two hierarchical levels of monitoring to ensure the integrity and the accuracy of aggregate result, only when necessary, i.e. only when malicious activities are detected. Relying on this new type of monitoring mechanism, the framework has the ability to recover from aggregator failure without neglecting energy efficiency, providing thus much higher availability than other security protocols.