Mobile ad-hoc networks (MANETs) are a specific kind of wireless networks that can be quickly deployed without pre- existing infrastructures. They are used in different contexts such as collaborative, medical, military or embedded applications. However, MANETs raise new challenges when they are used in large scale network that contain a large number of nodes. Subsequently, many clustering algorithms have emerged. In fact, these clustering algorithms allow the structuring of the network into groups of entities called clusters creating a hierarchical structure. Each cluster contains a particular node called cluster head elected as cluster head according to a specific metric or a combination of metrics such as identity, degree, mobility, weight, density, etc. MANETs has drawbacks due to both the characteristics of the transmission medium (transmission medium sharing, low bandwidth, etc.) and the routing protocols (information diffusion, path finding, etc.). Clustering in mobile ad hoc networks plays a vital role in improving resource management and network performance (routing delay, bandwidth consumption and throughput). In this paper, we present a study and analyze of some existing clustering approaches for MANETs that recently appeared in literature, which we classify as: Identifier Neighbor based clustering, Topology based clustering, Mobility based clustering, Energy based clustering, and Weight based clustering. We also include clustering definition, review existing clustering approaches, evaluate their performance and cost, discuss their advantages, disadvantages, features and suggest a best clustering approach

This paper proposes an explicit definition of green software requirements and a tool to support their evaluation. The proposed evaluation tool describes the green efficiency by considering the energy consumption as the main aspect to be studied during the development stage. This approach consists of building a multiple regression model, by using a supervised learning algorithm, in order to reproduce the energy consumption pattern of devices at different workload circumstances. The energy consumption model is then deployed to estimate the impact of software applications based on their resource usage. Our work has been validated on desktop and mobile devices. The experiments show the effectiveness of the proposed energy profiling tool that provided relevant information on the energy consumption of software applications.

A common phenomenon in everyday life is that, when a strange event occurs or is announced, a regular crowd can completely change, showing different intense emotions and sometimes uncontrollable and violent emerging behavior. These emotions and behaviors that disturb the organization of a crowd are of concern in our study, and we attempt to predict these suspicious circumstances and provide help in making the right decisions at the right time. Furthermore, most of the models that address crowd disasters belong to the physical or the cognitive approaches. They study pedestrian flow and collision avoidance, etc., and they use walking speed and angle of vision. However, in this work, based on a behavioral rules approach, we aim to model emergent emotion, behavior and influence in a crowd, taking into account particularly the personality of members of the crowd. For this purpose, we have combined the OCEAN (Openness, Consciousness, Extraversion, Agreeableness, and Neuroticism) personality model with the OCC (Ortony, Clore, and Collins) emotional model to indicate the susceptibility of each of the five personality factors to feeling every emotion. Then we proposed an approach that uses first fuzzy logic for the emotional modeling of critical emotions of members of the crowd at the announcement or the presence of unusual events, in order to quantify emotions. Then, we model the behavior and the tendency towards actions using probability theory. Finally, the influence among the members of the crowd is modeled using the neighborhood principle and cellular automata.

Structured peer-to-peer systems based on distributed hash table (DHT) have known a great popularity and performance since their appearance. They have experienced multiple improvements to increase the efficiency, like replication mechanism with different used methods and different objectives like increasing data availability or fitting to churn. Besides their benefits, these methods suffer from the excessive generated overhead in maintenance process. On the other side, we have the interest deployment of DHT overlay on mobile ad hoc network, which benefits from the infrastructure-less architecture, but presents some shortcomings because of the limited bandwidth and energy batteries, what require a reduced overhead. Therefore, the aim of this work is to improve the lookup efficiency of DHT-based Chord on mobile ad hoc network underlay. For that, we propose a novel replication mechanism based on data structure to determine the replica nodes, while avoiding the excessive generated overhead in maintenance process to cope with the problem of limited energy batteries. To evaluate the proposed method, we present an extensive simulation study that compares the work to another efficient replication method and to mobile basic Chord. The results show the efficiency of our approach in decreasing the lookup path, the maintenance overhead, and the energy consumption.

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