Manoj Lall

A review of service discovery schemes in wireless mesh networks

The increase in number of users in Wireless Mesh Networks (WMNs) setups consequently represents an upsurge in numbers of services. Services such as internet, e-commerce, audio streaming, Voice over Internet Protocol (VoIP), Video on Demand (VoD), file and printer sharing among others will be clogged and ran over WMNs. This further leads to poor Quality of Service (QoS). Quick and timely discovery of these services becomes an essential parameter in optimizing performance of these networks. In this paper therefore, we present an overview of the various existing service discovery schemes in WMNs. We also present the various gaps available in these schemes for future service discovery schemes.

Performance Optimization of Intelligent Home Networks

Home networks continue to experience an increase in the number of devices and services. This increase has come as a result of rapid technological revolutions in engineering and telecommunication industries. The advancement in technology has enabled access of intelligent home networks locally as well as remotely. This in turn has led to poor quality of service (QoS) to the consumers of such services and applications. Therefore, in this chapter, we present performance optimization of intelligent home network model that is scalable and adaptable to these increases and technological changes. We segmented and prioritized the intelligent home network into six subnets. Then we assigned weighing factor numbers to the devices, which aided in their classification and prioritization. We then grouped the supported home network services and applications into six classes and increased the number of transmitted packets per iteration in each Class of Service (CoS). We tested and evaluated proposed model using OMNET++ simulator against Priority Queuing (PQ) and Class-Based Weighted Fair Queuing (CBWFQ) models. The results show an average network packet throughput of 99.74 %, delay of 3.02 s, and loss of 1.59 %.

A cloud computing augmenting agricultural activities in Marginalized Rural Areas: A preliminary study

Marginalized Rural Areas (MRAs) practice farming and in most cases lack basic resources and skills to improve yields, which are often poor. This has led to famine, poverty, crime and rural to urban migration. Agricultural activities when practiced very well can alleviate such challenges in the country. We are therefore, developing a cloud computing model that seeks to improve agriculture as an activity in MRAs. It shall include a cloud architecture which is a Mobile information system and would be used by farmers (including subsistence farmers) to share (upload and download) information about the farming techniques, markets, weather, seeds, etc. In this paper, we present findings of a preliminary study of what common mobile devices are mostly used and what type of agriculture is mostly practiced by MRAs people. We collected data through close ended questionnaires that were given to farmers in Pongola in KwaZulu-Natal province of South Africa. The preliminary study results show that 54% of farmers face challenges of pests and birds, 26% weather and 20% diseases affecting their livestock. 94% of farmers own mobile devices of which 59% of them are feature phones. 58% of the mobile phone owning farmers do not use them to access information regarding farming and lastly the most practiced type of agriculture in MRAs is subsistence farming with 97%, where famers focus on growing enough food to feed their own consumption.

Modeling of Service Discovery Over Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have played a huge rule in networking environments by supporting seamless connectivity, Wide Area Networks (WANs) coverage, mobility features, etc. However, the rapid increase in the number of consumers on these networks brought an upsurge in competitions for available services and resources. This has led to link congestions, data collisions, and link interferences, which affects Quality of Service (QoS) . Therefore, the quick and timely discovery of the services and resources becomes an essential parameter in optimizing the performance of service discovery on these networks. In this study, we present Ndlovu Okuthe Manoj (NOM) model, a service discovery model that integrates the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. The PSO is used to dynamically define and give different priorities to services on the network, based on varied workflow procedures. On the other hand, the ACO is used to effectively establish the most cost-effective path whenever each transmitter has to be searched to identify whether it possesses the requested service(s). Furthermore, we design and implement the Link Collision Reduction (LCR) algorithm. It’s objective is to define the number of service receivers to be given access to the services simultaneously. We then simulate the proposed model in Network Simulator 2 (NS2), against Ant Colony based-multi constraints QoS-aware service selection (QSS) and FLEXIble Mesh Service Discovery (FLEXI-MSD) models. The results show an average service discovery throughput of 80%, service availability of 96%, service discovery delay of 1.8 s, and success probability of service selection of 89%.

Towards a Model of Enhancing Safety Fishing in South Africa

In South Africa, fishing industry helps reduce poverty, famine and crime by providing jobs to citizens. Fishing industry also contributes to income generation of the country through export markets, investments from private companies, etc. However, the industry faces a mirage of challenges due to unsafe fishing conditions caused by weather, criminals, wild animals, faulty boats, etc. In other words, the benefits we get from fishing are affected by these unsafe conditions. We intend to implement a system that would enhance safe fishing by reducing unsafe fishing conditions using Java, Android, MySQL, and Toad technologies. In order to model our system, we went to Eastern Cape, Limpopo, Mpumalanga, Western Cape, Northern Cape and Kwazulu Natal provinces to collect data using questionnaires. Research questions included, what methods fishermen use to catch fish, what challenges fishermen face while fishing, etc. After collecting all the required data, we modelled the system using UML diagrams. The results show real challenges and safety concerns for fishermen in South Africa.

An Improved-Cross Layer Scheduling model for intelligent home networks

We daily add more devices and services into existing intelligent home networks. Consequently, various networking standards evolutions being experienced world over have not left home networks behind. These evolutions results in competition and depletion of the available limited resources. Consumers therefore, experience unavailable, unreliable and poor performing network both locally and remotely. In this paper, we present an Improved-Cross Layer Scheduling (CLS) model that optimizes performance of intelligent home networks. We have used Particle Swam Optimization (PSO) algorithm to dynamically schedule, align and prioritize network subnets, devices and services in the model. We have used Virtual Local Area Network (VLAN) protocol to classify home network into six subnets. Consequently, we have used weighing factor numbers to classify subnet devices. Further, we have used Differentiated Service Code Points (DSCP) to classify supported home network services into six classes. Moreover, we have increased number of packets transmitted per Class of Service (CoS) iteratively. We have optimized each subnet and used the output of the preceding subnet as input to subsequent subnet. Equally, we have reduced delay between consecutive transmitting CoSs in the media. We have simulated our model and realized average network throughput of 99.735%, packet loss of 1.59% and delay of 1.82 milliseconds.

A Priority-Based Service Discovery Model Using Swarm Intelligence in Wireless Mesh Networks

The ever increasing number of users in Wireless Mesh Networks (WMNs) setups consequently represents an upsurge in competitions for available services. Consequently, services are clogged and ran over WMNs, which further leads to poor Quality of Service (QoS). Quick and timely discovery of available services becomes an essential parameter in optimizing performance of WMNs. In this paper therefore, we present a Priority-based Service Discovery Model (PSDM) using Swarm Intelligence in WMNs. We use the Particle Swarm Optimization (PSO) algorithm to dynamically define and prioritize services supported by the network. Additionally, the Ant Colony Optimization (ACO) algorithm is used to choose the shortest path when each transmitter has to be searched to identify if it possesses the requested services. We have designed and implemented the PSDM using Network Simulator 2 (NS-2) tool. Consequently, we realized throughput of 80%, service availability of 96% in some instances, and an average delay of 1.8 ms.

Classification of Water Pipeline Failure Consequence Index in High-Risk Zones: A Study of South African Dolomitic Land

Increasing numbers of pipeline breakdown experienced by utilities undoubtedly raise alarms concerning the anticipated failure consequences. Seemingly mild, these consequences can however, fluctuate to severe or fatal, especially in high risk locations. Utility personnel are therefore pressured to employ up-to-par operational policies in attempt to minimize possible fatalities. This however, may be overwhelming considering inherent uncertainties that make it difficult to understand and adapt these consequences into utilities’ risk management structure. One way of handling such uncertainties is through the use of Bayesian Networks (BNs), which can comfortably combine supplementary information and knowledge. In this paper therefore, we present an overview of the causes and impacts of pipeline failure. We aggregate and classify failure consequences in a select high risk zone into four indexes; and finally, we outline how BNs can accommodate these indexes for pipeline failure prediction modeling. These indexes function as effective surrogate inputs where data is unavailable.