The challenging problem of energy efficiency in security of the Internet of Things (IoT) is tackled in this article. The authors consider the upcoming generation of mobile networks, 5G, as a communication architecture for the IoT. The concept of adaptive security is adopted, which is based on adjusting the security level as per the changing context. It has the potential of reducing energy consumption by adapting security rather than always considering the worst case, which is energy consuming. The consideration of 5G introduces new dynamics that can be exploited to perform more adaptation. The proposed solution introduces an intelligence in the application of security, from the establishment phase to the use phase (end-to-end). The security level related to the used cryptographic algorithm/key is adapted for each node during the establishment phase, so to match with the duration of the provided services. A new strategy is formulated that considers both IoT and 5G characteristics. In addition, a solution based on the framework of the coalitional game is proposed in order to associate the deployed objects with the optimized security levels. Moreover, the application of security is also adapted during the use phase according to the threat level. Trust management is used to evaluate the threat level among the network nodes, while existing works focus on performing the adaptation during the use phase. The proposed approach achieves more adaptation through the consideration of both IoT and 5G dynamics. The analysis and performance evaluations are conducted to show the effectiveness of the proposed end-to-end approach.

Recently, palmprint recognition in forensic domain has gained considerable attention since 30 % of evidence left in crime scene originate from palms. Like most of recognition systems, palmprint one is composed of three steps: preprocessing, features extraction and representation and finally features matching. Minutiae are the most reliable and discriminating features used in these systems. Minutiae matching is then very critical. Quantifying the similarity between two sets of extracted minutiae and assigning a score is particularly important in this step. In this paper, we designed similarity scores for a minutiae based recognition system using a minimum of extracted information. Our proposed scores are based on the score of [1], used in point pattern matching. They are tested and compared on the database used in [2]. The best one is tested and compared to the one presented in the same work [2]. Obtained results are very interesting.

Air pollution has major negative effects on both human health and environment. Thus, air quality monitoring is a main issue in our days. In this paper, we focus on the use of mobile WSN to generate high spatio-temporal resolution air quality maps. We address the sensors' online redeployment problem and we propose three redeployment models allowing to assess, with high precision, the air pollution concentrations. Unlike most of existing movement assisted deployment strategies based on network generic characteristics such as coverage and connectivity, our approaches take into account air pollution properties and dispersion models to offer an efficient air quality estimation. First, we introduce our proposition of an optimal integer linear program based on air pollution dispersion characteristics to minimize estimation errors. Then, we propose a local iterative integer linear programming model and a heuristic technique that offer a lower execution time with acceptable estimation quality. We evaluate our models in terms of execution time and estimation quality using a real data set of Lyon City in France. Finally, we compare our models' performances to existing generic redeployment strategies. Results show that our algorithms outperform the existing generic solutions while reducing the maximum estimation error up to 3 times.

Providing efficient security services in dynamic low-power environments as the Internet of Things (IoT) is a challenging task. The deployment of static security services will consume the energy even if it is not required in some situations, so this induces a waste of resources. In this paper, we introduce an efficient model for adaptive security in the IoT based on trust management. Most of existing adaptive security approaches lack of practical means to evaluate threats. On the other hand, trust management systems are designed to deal with selfish behaviors or internal attacks and not to assist cryptographic measures. Our solution evaluates the trust level related to the presence of security threats among nodes, and adapt consequently cryptographic measures. The obtained simulation results show that our solution reduces considerably energy consumption and remains yet secure.

In this paper, we introduce Adaptive Data-Communication Trust Mechanism for clustered wireless sensor networks (ADCT) to effectively deal with compromise or malicious nodes. Unlike prior works, we propose an adaptive function to evaluate the direct trust between nodes according to the need of the application in terms of trust severity. We also consider data trust to cope with untrustworthy nodes during the data collection despite their communication capabilities. Finally, we introduce a new method to discard a malicious recommendation when building feedback at the base station or cluster-head level. Theoretical analysis shows that our trust mechanism allows a good trade-off between memory requirement and trust cooperation.

Palmprint is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition mode can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactless devices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose a ROI extraction method that addresses this issue. We test our method on two databases PolyU and CASIA which illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases. Performances obtained with the proposed palmprint system are promising.

We consider the problem of failure detection in networks with energy and communication constraints. Most of current implementations of unreliable failure detectors (FD) use mechanisms to notify process failures in fully connected networks with reliable communication links. This assumption is not applicable to lossy networks. Furthermore, such implementations do not consider resource limitations in terms of energy, storage and bandwidth. This paper presents a new failure detection model for Low-power Lossy Networks (LLN). Our approach defines an adaptive timer-based paradigm. Besides, we introduce two techniques based on the proposed general model. We evaluate all contributions using implementation on Omnet++/Mixim framework. Simulation results demonstrate that our FD enhances detection performances for LLN compared to traditional FD.

Tree topologies are widely used in WSN in order to route convergecast traffic to the sink. We consider in this paper the Shortest Path routing Tree (SPT) problem in WSN under different metrics; we show that the basic SPT based strategies are unsuitable for the many-to-one WSN when considering some metrics to compute link costs. Indeed, existing SPT approaches aim to construct a tree rooted at the sink such that the cost of the path from any node to the sink is minimal, while the cost of a given path is computed as summation of the costs of links that compose this path. However, in many-to-one WSN, links which are close to the sink are more critical than other links when using some metrics. We propose in this paper a new weighted path cost function, and we show that our cost function is more suitable for WSN. Based on this cost function, we propose a simple and efficient weighted shortest path tree construction which does not introduce new overheads. We consider, then, the particular case of energy-aware routing in WSN when we apply our new solution in order to construct more suitable energy-aware SPT. We conduct extensive simulations which show that our approach allows to enhance the network lifetime up to 17% compared to the basic one.

The Multi-Wireless-Agent-Communication (MWAC) model uses a multiagent self-organization process through to manage communications in wireless sensor networks. This paper presents a first improvement of the MWAC model using an ant colony optimization. We introduce the use of ant colony optimization in the context of multiagent systems and wireless sensor networks. Ants are not simply deployed on the multiagent society. Each ant interacts with agents to obtain the best compromise on the routes choice to enable a reliable communication.