Mustapha Boushaba

High accuracy localization method using AoA in sensor networks

In sensor networks, several applications such as habitat monitoring and moving objects tracking, require the knowledge of nodes positions. Position estimation most often includes errors due to the measurements of distance and incoming angles between neighbors. Erroneous positions are propagated from a node to other nodes exacerbating the degree of errors in the estimation of the positions of these nodes. In this paper, we propose a new localization method, called HA-A2L (High Accuracy localization based on Angle to Landmark); it consists of (a) a new protocol that allows nodes to exchange information pertinent to the localization process; and (b) a localization algorithm that uses estimation of distances and incoming angles to locate nodes in sensors networks. Compared, via simulations, to previous methods, such as APS and A2L, HA-A2L considerably increases the number of located nodes with far better accuracy.

Source-Based Routing in Wireless Mesh Networks

Wireless mesh networks (WMNs) are currently used to provide broadband access to the Internet anytime and anywhere. Generally, WMNs consist of mesh routers equipped with one or more interfaces allowing connectivity to the Internet through gateways. To route traffic from sources to destinations, many routing protocols have been proposed in the literature. However, most of them take into account at most one metric (e.g., interferences, packet losses, and load at gateways). Moreover, almost all of these schemes consider only one type of interferences: interflow or intraflow. In this paper, we propose a new source routing and gateway selection scheme, which is called source-based routing (SBR), that improves the performance of WMNs. SBR uses a novel routing metric, which is a combination of packet losses, intraflow and interflow interferences, and load at gateways, to select best paths to reach selected gateways. Simulation results show that the proposed SBR improves the network performance and outperforms existing routing schemes, which are based on expected transmission count (ETX), nearest gateway (NG; i.e., shortest path to gateway), load at gateways (LG), or interference ratio (IR); more specifically, SBR yields 33%, 26%, 13%, and 10% more throughput compared with LG, ETX, NG, and IR, respectively.

Performance Management of IEEE 802.15.4 Wireless Sensor Network for Precision Agriculture

The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters (macMinBE and macMaxCSMABackoffs) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements.

Best path to best gateway scheme for multichannel multi-interface wireless mesh networks

This paper addresses the problem of optimal gateways selection and route selection to Internet in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to Internet. Several schemes have been proposed to route packets in WMNs or to select appropriate gateways to connect clients to Internet. However, most of these schemes consider packet loss, interferences, load at gateways, or ETX (Expected Transmission Count) as routing metrics; only a few schemes consider two some of these metrics at the same time. In this paper, we propose an efficient gateway and path selection scheme, called BP2BG (Best Path to Best Gateway), that takes into account load at gateways, ETX and interferences in order to select best path to best gateway. Simulation results show that BP2BG can significantly improve the overall network performance compared to schemes using either ETX, nearest gateway (i.e., shortest path to gateway), load at gateways or interferences as metrics for path and gateway selection.

Reinforcement learning-based best path to best gateway scheme for wireless mesh networks

This paper addresses the problem of optimal routing in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet.

QoS-aware resource allocation for mobile media services in cloud environment

With the advancement of technology in smartphones and their popularity, users are expecting higher performance and functionality from mobile devices just as powerful non-mobile devices, especially in the context of multimedia applications. However, despite significant advances in handheld devices, there still exists a gap between device capabilities and the requirements of applications. Mobile Cloud Computing emergence has overcome the barriers of the mobile device restrictions. To improve the performance of media services processed in clouds, an efficient resource allocation mechanism is needed. In this paper, we propose a resource allocation scheme in Mobile Cloud Computing based on Learning Automata technique, taking into account three fundamental concerns: total response time of various types of mobile media services, if they are to be executed on cloud; level of uncertainty (i.e. prone to failure measurement); and computational capacity requirement of services. Simulation results demonstrate that the proposed resource allocation scheme can optimally allocate cloud resources to each media service while ensuring minimal QoS requirements of applications.

Reinforcement learning based routing in wireless mesh networks

This paper addresses the problem of efficient routing in backbone wireless mesh networks (WMNs) where each mesh router is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count and interference ratio. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously and considers traffic congestion around the gateways is needed. In this paper, we propose an adaptive scheme for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR), that (1) considers the critical areas around the gateways where mesh routers are much more likely to become congested and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio, load at the gateways and end-to end delay. Simulation results show that RLBDR can significantly improve the overall network performance compared to schemes using either Metric of Interference and Channel switching, Best Path to Best Gateway, Expected Transmission count, nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.

Local node stability-based routing for Wireless Mesh Networks

Thanks to their flexibility and their simple installation, Wireless Mesh Networks (WMNs) allow a low cost deployment of a network infrastructure. They can be used to extend the wired network coverage allowing connectivity anytime and anywhere. Network stability is a key performance metric in supporting real time communication over the network. Because of high bandwidth demand and dynamic traffic variation, several paths in WMNs are expected to be unstable. High levels of network instability can lead to interferences, packet losses and high delays. In this paper, we address the stability problem of WMNs; instability in these networks is caused mainly by link quality fluctuations and frequent route flapping. Indeed, most routing protocols try to optimize a routing metric locally or globally without considering network stability. First, we present the key factors that may cause network instability; then, we propose a new technique, called Local Node Stability-based Routing (LNS), using the entropy function (known as a measure of the uncertainty and the disorder in a system) to define a node stability. Simulation results show that the stability can be improved in WMNs using LNS compared to other routing schemes namely RLBDR, MIC and ETX.

HA-A2L: angle to landmark-based high accuracy localization method in sensor networks

In sensor networks, several applications such as habitat monitoring and moving objects tracking, require the knowledge of nodes positions. Position estimation most often includes errors due to the measurements of distance and incoming angles between neighbors. Erroneous positions are propagated from a node to other nodes exacerbating the degree of errors in the estimation of the positions of these nodes. In this paper, we propose a new localization method, called HA-A2L. Compared, via simulations, to previous methods, such as APS and A2L, HA-A2L considerably increases the number of located nodes with far better accuracy.

Node stability-based routing in Wireless Mesh Networks

Abstract Network stability is a key performance metric in supporting real time communication over wireless networks. Because of high bandwidth demand and dynamic traffic variation, several paths in Wireless Mesh Networks (WMNs) are expected to be unstable. High levels of network instability can lead to interferences, packet losses and high delays. In this paper, we address the stability problem of WMNs. Instability in these networks is caused mainly by link quality fluctuations and frequent route flapping. First, we present the key factors that may cause network instability. Then, we propose a new technique, called Node Stability-based Routing (NSR), using the entropy function to define a node stability and a probability function to select an appropriate gateway. Simulation results show that NSR can significantly improve the overall network performance compared to techniques using interference and channel switching (MIC), Expected Transmission count (ETX) or load at gateways as a routing metric, Reinforcement learning-based best path to best gateway (RLBDR), and nearest gateway (i.e., shortest path to gateway).