Mobility models towards the performance of geographical-based route maintenance strategy in DSR

In the future, Mobile Ad hoc Networks (MANET)s are expected to be deployed in myriads of scenarios having complex node mobility and connectivity dynamics. Unfortunately, these complex movement scenarios give a tough challenge to the MANET routing protocol. We reviewed some mobility models that have different mobility characteristic, and also studied the effect of these mobility models towards the performance of geographical-based route maintenance strategy in the Dynamic Source Routing (DSR) protocol

A new route maintenance strategy for dynamic source routing protocol

Although DSR can respond a route quickly, it yields a long delay when a route is rebuilt. This is because when source node receives RERR packet, it will try to find alternative routes from route cache. If alternative routes are not available, source node, then, will enter route discovery phase to find new routes. In this paper we introduce a new route maintenance strategy by utilizing location information. We called this new route maintenance strategy as DISTANCE (Distance baSed rouTe maintenANCE). DISTANCE works by adding another node (called bridge node) into the source list to prevent the link from failure. From the simulation result, DISTANCE improves the performance of DSR in terms of packet sending ratio and delay

Detection of denial of service attacks against domain name system using neural networks

In this paper we introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). Our system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a shorttime frame from traffic received by the target name server. We compared three different neural networks for detecting and classifying different types of DoS attacks. The proposed system is evaluated in a simulated network and showed that the best performed neural network is a feed-forward backpropagation with an accuracy of 99%

Coverage degree in mobile nodes wireless sensor network

Coverage degree and mobility have acquired a lot of attention lately in Wireless Sensor Network (WSN). This paper shows that nodes mobility can enhance coverage degree for WSN. Analytical model is introduced to describe the coverage degree in mobile nodes wireless sensor network. MATLAB was used to build the simulator. The analytical work is validated by simulated results. Both of analytical model and simulation showing that the coverage degree has been enhanced in Mobile Nodes Wireless Sensor Network (MNWSN) as compared to static network

Energy-aware cluster based cooperative spectrum sensing for cognitive radio sensor networks

Energy efficient spectrum sensing and data communication to extend the lifetime of cognitive radio sensor network is becoming increasingly important due to resource constraint of CR-WSN inherent from WSN. This paper presents an energy-aware clustering (EAC) algorithm that enhances spectrum sensing performance and reduces network energy consumption thereby prolonging lifetime of the network. We derived network wide energy consumption model in terms of spectrum sensing energy consumption, intra cluster and inter clusters energy consumptions, and then determined the optimal number of clusters for the network. Through numerical analysis, we evaluate the effectiveness of the proposed algorithm in terms of minimizing network wide energy consumption and improving spectrum sensing performance

An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach

A weighted hard combination scheme for cooperative spectrum sensing in cognitive radio sensor networks

Multi-user spatial sensing diversity exploration through cooperation spectrum sensing greatly improves sensing performance. However, high communication overhead and energy costs for exchanging sensing results may limit its viability in a realistic large scale resource constraint network such as cognitive radio wireless sensor networks. This paper presents a Weighted Hard Combination (WHC) scheme that combines features of both quantized and hard combining schemes to minimize energy cost for reporting sensing result and improve primary user detection performance in cooperative sensing. We evaluate the effectiveness of the scheme through simulation. Performance comparison of the WHC scheme in terms of detection performance, reporting energy cost and reporting time ratio with conventional hard combination, soft combination and quantized schemes indicates viability of the scheme. The results indicate that the WHC scheme minimizes reporting energy cost by 70% and improves detection performance by 5.6% compared to the quantized 3-bits scheme

Wireless sensor network for structural health monitoring: a contemporary review of technologies, challenges, and future direction

The importance of wireless sensor networks in structural health monitoring is unceasingly growing, because of the increasing demand for both safety and security in the cities. The speedy growth of wireless technologies has considerably developed the progress of structural monitoring systems with the combination of wireless sensor network technology. Wireless sensor network–based structural health monitoring system introduces a novel technology with compelling advantages in comparison to traditional wired system, which has the benefits of reducing installation and maintenance costs of structural health monitoring systems. However, structural health monitoring has brought an additional complex challenges in network design to wireless sensor networks. This article presents a contemporary review of collective experience the researchers have gained from the application of wireless sensor networks for structural health monitoring. Technologies of wired and wireless sensor systems are investigated along with wireless sensor node architecture, functionality, communication technologies, and its popular operating systems. Then, comprehensive summaries for the state-of-the-art academic and commercial wireless platform technologies used in laboratory testbeds and field test deployments for structural health monitoring applications are reviewed and tabulated. Following that, classification taxonomy of the key challenges associated with wireless sensor networks for structural health monitoring to assist the researchers in understanding the obstacles and the suitability of implementing wireless technology for structural health monitoring applications are deeply discussed with available research efforts in order to overcome these challenges. Finally, open research issues in wireless sensor networks for structural health monitoring are explored

Robust 3D indoor positioning system based on radio map using Bayesian network

Indoor positioning remains a serious issue due to the difficulty in attaining sufficient accuracy within an indoor environment using tracking technology of low complexity. Currently, most positioning systems do not embed the off-the-shelf (OTS) system which allows mobile devices to estimate location without using any additional hardware. In this paper, we propose a robust 3D indoor positioning system that is suitable for an indoor IoT application. This system based on Bayesian network that depends on Wi-Fi signals strength. It was experimentally tested in a building with pre-deployed access points (APs). The experimental results indicate that localization accuracy of the proposed system is high with the use of a small-sized radio map

TCP throughput optimization over 802.11 MAC protocol in multi-hop ad-hoc networks.

The acknowledgment strategy has great potential to increase the TCP throughput when it runs over 802.11 MAC protocol. In particular TCP acknowledgments carry out an extensive number of medium accesses as they compete in the same route as data packets for media. As the load increases, the well-known hidden terminal effects caused by interference between ACK and data packets in MAC layer can degrade TCP performance dramatically if TCP acknowledges every incoming data packets. In this paper, we propose a TCP-MAC interaction strategy which tries to reduce the number of induced ACKs by monitoring the channel condition. To this end, the total collision probability collected along the path in MAC layer is used to properly set the number of the delayed ACKs in TCP. Based on the measured collision probability, TCP sender dynamically adjusts itself to the channel condition by delaying less ACKs in high traffics and more in low traffic conditions. The simulation results show a reasonable tradeoff between performance under moderate and high traffics