Adaptive routing with intelligent portal in wireless mesh networks

The design of a routing protocol must be based on the characteristics of its target networks. The diversity of wireless networks motivates the design of different routing metrics, capturing different aspects of wireless communications. The design of routing metrics, however, is not arbitrary since it has a great impact on the proper operation of routing protocols. Combining a wrong type of routing metrics with a routing protocol may result in routing loops and suboptimal paths. We thoroughly study the relationship between routing metrics and routing protocols. Our work provides important guidelines for designing routing metrics and identifies the specific properties that a routing metric must have in order to be combined with certain type of routing protocols.

A cross-layer framework for joint routing and rate adaptation in infrastructure Wireless Mesh Networks

Routing is an essential mechanism for proper functioning of large networks, and routing protocols make use of routing metrics to determine optimal paths. The design of routing metrics is critical for achieving high throughput and we begin this chapter by proposing the design principles for routing metrics. These design principles are for ensuring the proper functioning of the network and achieving high throughput. We continue by giving a detail analysis of the existing routing metrics. We also look at the pitfalls of the existing routing metrics. We conclude the chapter by outlining the future research directions.

The stationary nature of nodes in a mesh network has shifted the main design goal of routing protocols from maintaining connectivity between source and destination nodes to finding high-throughput paths between them. In recent years, numerous link-quality-based routing metrics have been proposed for choosing high-throughput paths for unicast protocols. In this paper we study routing metrics for high-throughput tree or mesh construction in multicast protocols. We show that there is a fundamental difference between unicast and multicast routing in how data packets are transmitted at the link layer, and accordingly there is a difference in how the routing metrics for each of these primitives are designed. We adapt certain routing metrics for unicast for high-throughput multicast routing and propose news ones not previously used for high-throughput. We then study the performance improvement achieved by using different link-quality-based routing metrics via extensive simulation and experiments on a mesh network testbed, using ODMRP as a representative multicast protocol. Our testbed experiment results show that ODMRP enhanced with linkquality routing metrics can achieve up to 17.5% throughput improvement as compared to the original ODMRP.

Introduction: The future of healthcare systems relies on faster communication between sensors and smart devices, which can assist in taking decisions for betterment of patients. The responsiveness of wireless health care system in case of medical urgencies is crucial factor for survival of patient’s life. The routing protocol of infrastructure less based are playing key role in sending the information in such medical related emergencies. Therefore, it is important to study and identify the best routing protocol in wireless healthcare based system using Mobile Ad-hoc networks (MANETS). Methods: MANET routing protocols such as Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid Wireless Mesh Protocol (HWMP) through their routing environment are simulated through this research study. It is essential to highlight the effect of various attacks affecting the routing methodology of these protocols. Since Distributed Denial of Service (DDoS) attack is popularly talked about and is most vulnerable attack present in MANET environment, so this research study analyses AODV, SAODV and HWMP under DDoS attacks through various simulation parameters. Results: In this research study, most suitable routing protocols to handle DDoS attacks are simulated and estimated in terms of delay and packet delivery ratio in the scenario of changing nodes. This aids in providing implications to enhance existing protocols and alleviate the consequences of DDoS investigation by such attacks. Discussion: For ensuring the optimized routing, privacy and security of patient’s data during transmission in healthcare sector, MANET could be used as one of the important technology combined with IoT. For dealing with end to end data transmission of patient's sensitive data, MANET protocol plays a vital role in sending the information securely. Conclusion: The performance of AODV, SAODV and HWMP (Hybrid Wireless Mesh Protocol) are compared and tabularized which are the most popularly utilized protocols in healthcare environment. The simulation results shows that the HWMP out performed than AODV and SAODV routing protocol, in terms of evaluation metrics namely end-to-end delay and Packet Delivery Ratio (PDR) and could be considered as much less vulnerable against DDoS attacks prevailing in wireless healthcare sector using MANET.

Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network’s performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly. This paper presents the QL-Feed Forward routing algorithm (QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm (QFFR) can learn from the network environment and make routing decisions based on the algorithm’s learnings. The AI agent’s ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance.

Wireless sensor network (WSN) with several sensors is used to group measurements of certain physical quantities or environmental conditions, including sound, temperature, pressure, vibration, motion, or pollution, at various locations and ranges. In WSNs, several protocols address the issue of routing. Sensor nodes in WSN usually have limited energy resources and storage capacities. Therefore, the issue of energy usage in sensors and protocols is very important. This paper analyzes and compares the quality of service (QoS) performances of three important routing protocols of the mobile ad-hoc network (MANET) including Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), and destination sequenced distance vector (DSDV), in a smart building case study. The QoS evaluation metrics include residual energy of nodes, instant throughput (IT), average throughput (AT), packet delivery ratio (PDR), packet loss ratio (PLR), and route discovery latency (RDL) based on IEEE 802.15.4 MAC protocol standard. The simulation is carried out using NS2, and the WSN has 16 fixed and 4 mobility nodes with different speeds and paths. The simulation results illustrate that average throughput in AODV is 1.985118 (kbps), however, the figures for DSR and DSDV are 1.977780 and 1.720700 (kbps), respectively. PDR, also, in DSR stands at 1.0, but the figures for AODV and DSDV are lower with the range of 0.999572 and 0.997930, respectively. Overall, The DSR protocol provides a better performance compared to AODV and DSDV routing protocols in terms of PLR and PDR. Also, AODV has better efficiency in RDL and AT compared to other assumed protocols.

A multi-radio Wireless Mesh Network (WMN) is a multi-hop network that consists of mesh routers and mesh clients, where mesh routers are equipped with multiple radio interfaces and form the backbone of the mesh network. To provide backbone support, it is necessary to reduce interference and balance load in Wireless Mesh Networks (WMNs). We propose load-aware routing metric (LARM), which captures the difference in transmission rates, packet loss ratio, intra/inter-flow interference and traffic load in multi-radio mesh networks. The LARM metric helps finding paths that are better in terms of balancing load and reducing the inter-flow and intra-flow interference in multi-radio networks. Simulation results show that LARM metric significantly outperforms several existing routing metrics in multi-radio wireless mesh networks.

Although limiting the number of backhauls, specifically chosen transit access points (TAPs) that forward traffic from other TAPs, reduces the overall costs of a wireless mesh network (WMN), an egress bottleneck is induced, which aggregates traffic and limits the bandwidth. To avoid such problems while working to minimize budgetary expenses, we balanced traffic flow on ‘to‐be‐determined’ backhauls and adjacent links, a mixed nonlinear‐and integer‐programming problem that minimizes the aggregated flow subject to budget, backhaul assignment, top‐level load‐balanced routing, and link capacity constraints. Two algorithms are proposed, weighted backhaul assignment (WBA) and greedy load‐balanced routing (GLBR), that operate in conjunction with Lagrangean relaxation (LR), used for constructing LR‐based heuristics and also as a means of quantification and evaluation of the proposed algorithms. Experiment results show that the proposed algorithms achieve near‐optimization, outperforming related solutions. Copyright © 2008 John Wiley & Sons, Ltd.

This paper conduct a comprehensive simulation study on the behavior of TCP-friendly Rate Control (TFRC) protocol over Ad-Hoc On Demand Vector (AODV) routing protocol and Dynamic Source Routing (DSR) protocol. The aim are first, to measure the performance of TFRC in terms of throughput, delay and jitter. The second objective is to identify which Mobile Ad-Hoc Network (MANET) routing protocols work well with TFRC. We conduct the experiment by sending multimedia streaming traffic carried by TFRC over AODV and DSR respectively. Random-Waypoint mobility model is used for both experiments. Results obtained shows that in average TFRC over AODV has more throughput and less delay as compared with TFRC over DSR. However, in terms of jitter, TFRC over DSR gives better result.

The impact of interference-aware routing metrics on video streaming in Wireless Mesh Networks

In Wireless Sensor Networks (WSNs), fair utilisation of the network resources is a challenging task. In this regard, one of the determining factors is the routing protocol that plays an important role to route the data traffic from the source to the destination, under limited network resources. Most of the routing protocols and their variants in WSNs replace the routing metric of the prevalent routing protocol with their proposed routing metric and keep the route discovery mechanism intact. However, replacing only the routing metric is insufficient to improve the performance of the entire network as it can generate high End-to-End (ETE) delay in finding the destination as per the routing criteria. In this paper, a novel single path loop-free Distributed On-demand Multi-Optional routing (DOMO) protocol is presented. The DOMO selects the best sensor node based on the routing metric(s) amonst various alternatives at each stage during forward route formation. A simulation has been performed in ns-2.35 and it has been observed that, using the same routing metric(s), DOMO performs better for long-term communication in WSNs as compared to Ad hoc On Demand Distance Vector (AODV). The results have revealed that DOMO improves the average ETE delay by 17% and normalises the routing load by 47% as compared to AODV.

Wireless mesh networks (WMNs) offer a pragmatic solution with a cost-effective ratio when provisioning ubiquitous broadband internet access and diverse telecommunication systems. The conceptual underpinning of mesh networks finds application not only in IEEE networks, but also in 3GPP networks like LTE and the low-power wide area network (LPWAN) tailored for the burgeoning Internet of Things (IoT) landscape. IEEE 802.11s is well known for its facto standard for WMN, which defines the hybrid wireless mesh protocol (HWMP) as a layer-2 routing protocol and airtime link (ALM) as a metric. In this intricate landscape, artificial intelligence (AI) plays a prominent role in the industry, particularly within the technology and telecommunication realms. This study presents a novel methodology for the computation of routing metrics, specifically the ALM. This methodology implements the network simulator ns-3 and the RStudio as a statistical computing environment for data analysis. The former has enabled for the creation of scripts that elicit a variety of scenarios for WMN where information is gathered and stored in databases. The latter (RStudio) takes this information, and at this point, two linear predictions are supported. The first uses linear models (lm) and the second employs general linear models (glm). To conclude this process, statistical tests are applied to the original model, as well as to the new suggested ones. This work substantially contributes in two ways: first, through the methodological tool for the metric calculation of the HWMP protocol that belongs to the IEEE 802.11s standard, using lm and glm for the selection and validation of the model regressors. At this stage the ANOVA and STEPWIZE tools of RStudio are used. The second contribution is a linear predictor that improves the WMN’s performance as a priori mechanism before the use of the ns-3 simulator. The ANCOVA tool of RStudio is employed in the latter.

Routing metric plays a vital role in path selection process. A variety of routing metrics have been proposed to enhance the performance of routing protocols. Considering the forwarding behavior of neighbor node in the design of a routing metric would address the most generic packet dropping attacks in wireless mesh network (WMN). The existing routing metrics to address packet dropping attack in WMN only consider selfish mesh routers. On the other hand, to the best of our knowledge no such routing metric has been proposed to address the malicious behavior of mesh routers in a network. In this paper, we propose an airtime link metric AFC (Airtime Forwarding Capability), that considers both selfish and malicious behavior of mesh routers. AFC is computed using local station level measurements and does not require direct cooperation from any neighbor node, thereby avoiding neighbor lying attacks. The analysis of proposed metric shows that it satisfies the required design properties of a routing metric. The experimental results confirm the performance enhancement by accounting for the malicious behavior of mesh routers.

Wireless mesh network is a network implemented over a wireless local area network (LAN), and its infrastructure type is decentralized, relatively inexpensive, reliable, and resilient since the wireless routers are often static and not powered by batteries. In addition, the access points (routers) seldom change their positions in wireless mesh networks, and some wireless nodes have gateway functions to provide the connectivity to the Internet. Therefore, such networks behave almost like wired networks with infrequent topology changes and limited node failures. Due to these good properties and potential applications such as providing commercial Internet access to residents and local businesses, wireless mesh networks have drawn a lot of attention in recent years. For example, wireless mesh networks are being used as the last mile for extending the Internet connectivity for mobile nodes. In addition, some commercial deployments of multihop wireless meshes are already in the works. For instance, a number of mesh networks have been deployed in some cities of the United States, including Medford, Oregon, and cities in Minnesota [4]. Unlike traditional wired network, close-by mesh routers share certain wireless channels and the capacity of wireless mesh network will be increased tremendously when extending from a single channel model to a multiradio, multichannel, multihop model. For example, if two nodes vi and v j could communicate with each other by channel f1, and both nodes have at least one more available network interface cards, which could operate on another channel f2, if f2 is also available for both nodes, vi and v j could use both f1 and f2 to communicate simultaneously. When such cases are applied to more wireless nodes, the throughput of a wireless network will be increased tremendously.