Queue Length and Mobility aware Routing Protocol for Mobile Ad hoc Network

A mobile ad-hoc network (MANET) is different from other wireless networks in many ways. One of the key differences is that a MANET is a multihop wireless network,i.e., a routing path is composed of intermediate mobile nodes and wireless links connecting them. In this paper, heterogeneous Mobile Ad-hoc Networks (H-MANETs) are considered. H-MANETs are composed of nodes with different transmission range. We propose an improvement of AODV protocol called AMAODV (Adaptative Mobility aware AODV). This protocol is based on new metric combine more routing metrics (distance, relative velocity, queue length and hop count) between each node and one hop neighbor. Which permits to avoid losing route. Through the simulation, it is confirmed that this improvement has higher packet delivery ratio and less average end-to-end delay than basic AODV protocol.

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted

A Routing Protocol Based on Mobility Prediction for Mobile Ad Hoc Networks

In Mobile Ad hoc Networks (MANETs), where nodes have limited transmitting power, the transmission is typically multi-hop. The network topology changes frequently due to the unpredictable movement of mobile nodes because each node is free to move arbitrarily with different speeds. Thus, when one node enters in the transmission range of another node a link between those two nodes is established, and an existent link is broken when either node is out of the transmission range of the other. We refer as link duration, the time interval during in which the link still established. This paper presents a novel mobility metric for mobile ad hoc networks, called link duration (LD) that measures the stability of an active link. This mobility metric is introduced to represent relative mobility between nodes in multi-hop distance

Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover

In this paper, we address the problem of cell association during a handover performed in a dense heterogeneous network, where the preference of a mobile user’s equipment in terms of uplink traffic is not the same as for the downlink traffic. Therefore, since mobility is an intrinsic element of cellular networks, designing a handover from the perspective of the uplink and downlink is mandatory in the context of 5G cellular networks. Based on this arena, we propose a decoupled uplink-downlink handover scheme while making use of femtocells in order to maximize the overall network entity utilities and avoid overloading macrocells. However, the fact that the handover process is performed in a dense heterogeneous network makes the issue NP-hard. Therefore, taking into account the need for self-organizing solutions, we modeled the handover process as a matching game with externalities. Thus, we will provide an aspect of intelligence for the execution of the handover process to mobile user’s equipment (UE). To make the proposition more efficient, we integrate an assignment step to assist the matching game. Hence, the base stations will be investigated and filtered, keeping only the helpful base stations as the players in terms of the quality of service for the uplink and downlink. The numerical results verify the superiority of the proposed context-aware algorithm over traditional downlink handover and traditional decoupled uplink and downlink handover schemes, by improving the load balancing, increasing rates and reducing delays

A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks

The unexpected change in user equipment (UE) velocity is recognized as the primary explanation for poor handover quality. In order to resolve this issue, while limiting ping-pong (PP) events we carefully and dynamically optimized handover parameters for each UE unit according to its velocity and the coverage area of the access point (AP). In order to recognize any variations in velocity, we applied Allan variance (AVAR) to the received signal strength (RSS) from the serving AP. To assess our approach, it was essential to configure a heterogeneous network context (LTE-WiFi) and interconnect Media-Independent Handover (MIH) and Proxy Mobile IPv6 (PMIPv6) for seamless handover. Reproduction demonstrated that our approach does not only result in a gain in relatively accurate velocity but in addition reduces the number of PP and handover failures (HOFs)

Analysis of the Deployment Quality for Intrusion Detection in Wireless Sensor Networks

The intrusion detection application in a homogeneous wireless sensor network is defined as a mechanism to detect unauthorized intrusions or anomalous moving attackers in a field of interest. The quality of deterministic sensor nodes deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. An area may require that multiple nodes monitor each point from the sensing area; this constraint is known as k-coverage where k is the number of nodes. The deployment quality of sensor nodes depends directly on node density and sensing range; mainly a random sensor nodes deployment is required. The major question is centred around the problem of network coverage, how can we guarantee that each point of the sensing area is covered by the required number of sensor nodes and what a sufficient condition to guarantee the network coverage? To deal with this, probabilistic intrusion detection models are adopted, called single/multi-sensing detection, and the deployment quality issue is surveyed and analysed in terms of coverage. We evaluate the capability of our probabilistic model in homogeneous wireless sensor network, in terms of sensing range, node density, and intrusion distance

Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images