Channel acquisition and routing system for real-time cognitive radio sensor networks

The need for efficient spectrum utilization and routing has ignited interest in the Cognitive Radio Sensor Network (CRSN) paradigm among researchers. CRSN ensures efficient spectrum utilization for wireless sensor network. However, the main challenge faced by CRSN users have to deal with is the issue of service quality in terms of interference when using channels and degradation in multi-hop communication. This thesis proposes to overcome the interference due to contention and routing issues through the design of an efficient Channel Acquisition and Reliable routing System (CARS). CARS is designed to reduce carrier sense multiple access contention and enhance routing in CRSNs. CARS comprises of Lightweight Distributed Geographical (LDG), and Reliable Opportunists Routing (ROR) modules. LDG is a medium access control centric; cross-layer designed protocol to acquire a common control channel for signalling to determine the data channel. ROR is a network-centric cross-layer designed protocol to decide on a path for routing data packets. The result shows that LDG significantly reduces the overhead of media access contention and energy cost by at an average of 70% and 80% respectively compared to other approaches that use common control channel acquisition like Efficient Recovery Control Channel (ERCC) protocol. In addition, LDG achieves a 16.3% boost in the time to rendezvous on the control channel above ERCC and a 36.9% boost above Coordinated Channel Hopping (CCH) protocol. On the other hand, the virtual clustering framework inspired by ROR has further improved network performance. The proposed ROR significantly increases packet received at the sink node by an average of over 20%, reduces end-to-end latency by an average of 37% and minimizes energy consumption by an average of 22% as compared to Spectrum-aware Clustering for Efficient Multimedia routing (SCEEM) protocol. In brief, the design of CARS which takes the intrinsic characteristics of CRSNs into consideration helps to significantly reduce the energy needed for securing a control channel and to guarantee that end-to-end, real-time conditions are preserved in terms of latency and media content. Thus, LDG and ROR are highly recommended for real-time data transmission such as multimedia data transfer in CRSN

Classified Medium Access Control Algorithm (CL-MAC) for Enhanced Operation of IEEE 802.11ah

We present in this apaper a high level framework of a proposed Medium Access Control Algorithm known as Classified Medium Access Control Algorithm for enhanced operation of IEEE 802.11ah.  IEEE 802.11ah is an amendment for the IEEE 802.11 standard known as Wireless Local Area Network (WLAN) or Wi-Fi network standard. This amendment was mainly established to increase the number of Wi-Fi stations managed by the single Access Point. As more and more number of heterogeneous network stations emerge to also utilize this network, some techniques have been employed to ensure better management of the network but this still remains an open issue that needs to be tackled. This paper presents a hybrid TDMA and CSMA/CA scheme for the channel access in lieu of the default Enhanced Distributed Channel Access (EDCA) of the WLAN. When compared with the result of the EDCA, the proposed scheme provided a better throughput performance for the IEEE 802.11ah amendment

Routing Protocols for Wireless Multimedia Sensor Network: A Survey

Multimedia applications have become an essential part of our daily lives, and their use is flourishing day by day. The area of wireless sensor network is not an exception where the multimedia sensors are attracting the attention of the researchers increasingly, and it has shifted the focus from traditional scalar sensors to sensors equipped with multimedia devices. The multimedia sensors have the ability to capture video, image, audio, and scalar sensor data and deliver the multimedia content through sensors network. Due to the resource constraints nature of WSN introducing multimedia will add more challenges, so the protocols designed for multimedia wireless sensor network should be aware of the resource constraints nature of WSN and multimedia transmission requirement. This paper discusses the design challenges of routing protocols proposed for WMSN. A survey and comprehensive discussion are given for proposed protocols of WMSN followed by their limitations and features

Application of bat algorithm for the detection of hidden nodes in IEEE802.11ah networks

The occurrence of the hidden node problem in IEEE802.11ah has increases by 41% as compared to previous versions of IEEE802.11 standards. This makes IEEE802.11ah network to be prone to experience high collision and low throughput. Previous efforts to solve this problem has mainly not addressed the issue of locating potential hidden nodes in the network. As a result, the hidden node problem in IEEE802.11ah still remains an open issue. This paper proposes an algorithm that applies bat algorithm for detecting hidden nodes in IEEE802.11ah networks. Our results have shown the effectiveness of this algorithm in detecting hidden nodes. This algorithm can be used to properly manage communication in IEEE802.11ah

VHF band utilization measurement for cognitive radio application in Malaysia

This paper presents an experimental quantitative analysis of the dynamic behavior of very high frequency (VHF) III at 174–230 MHz band for opportunistic access in Malaysia. In order to investigate the variations of the VHF channels with respect to all operating stations in the country, measurements were conducted at two locations, namely, Malaysian Institute of Microelectronic Systems and University of Tenaga Nasional, Malaysia. The experimental result shows that the overall spectrum utilization for the two locations is just about 32 and 7 % respectively. These experimental results reflect a low utilization of the VHF spectrum band which consequentially indicates the ample availability of TV white space which can be utilized via opportunistic access for services like the Internet and other services. Primarily, this work is an attempt to obtain a set of generic reference values for evaluation of spectrum usage, which can be used for other potential bands for the future deployment of secondary user applications in Malaysia. It also provides useful information to the regulator in considering the deployment of opportunistic spectrum access, which can significantly impact both economic and social communication in the country

Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks

The emergence of the Internet of Things and the proliferation of mobile wireless devices has brought the area of mobile cognitive radio sensor networks (MCRSN) to the research spot light. Notwithstanding the potentials of CRSNs in terms of opportunistic channel usage for bursty traffic, the effect of the mobility of resource-constrained nodes to route stability, mobility-induced spatio-temporal spectral opportunities and primary user (PU) protection still remain open issues that need to be jointly addressed. To this effect, this paper proposes a mobile reliable geographical forwarding routing (MROR) protocol. MROR provides a robust mobile framework for geographical forwarding that is based on a mobility-induced channel availability model. It presents a comprehensive routing strategy that considers PU activity (to take care of routes that have to be built through PU coverage), PU signal protection (by the introduction of a mobility-induced guard (mguard) distance) and the random mobility-induced spatio-temporal spectrum opportunities (for enhancement of throughput). It also addresses the issue of frequent route maintenance that arises when speeds of the mobile nodes are considered as a routing metric. As a result, simulation has shown the ability of MROR to reduce the route failure rate by about 65% as against other schemes. In addition, further results show that MROR can improve both the throughput and goodput at the sink in an energy-efficient manner that is required in CRSNs as against compared works

Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks

The emergence of the Internet of Things and the proliferation of mobile wireless devices has brought the area of mobile cognitive radio sensor networks (MCRSN) to the research spot light. Notwithstanding the potentials of CRSNs in terms of opportunistic channel usage for bursty traffic, the effect of the mobility of resource-constrained nodes to route stability, mobility-induced spatio-temporal spectral opportunities and primary user (PU) protection still remain open issues that need to be jointly addressed. To this effect, this paper proposes a mobile reliable geographical forwarding routing (MROR) protocol. MROR provides a robust mobile framework for geographical forwarding that is based on a mobility-induced channel availability model. It presents a comprehensive routing strategy that considers PU activity (to take care of routes that have to be built through PU coverage), PU signal protection (by the introduction of a mobility-induced guard (mguard) distance) and the random mobility-induced spatio-temporal spectrum opportunities (for enhancement of throughput). It also addresses the issue of frequent route maintenance that arises when speeds of the mobile nodes are considered as a routing metric. As a result, simulation has shown the ability of MROR to reduce the route failure rate by about 65% as against other schemes. In addition, further results show that MROR can improve both the throughput and goodput at the sink in an energy-efficient manner that is required in CRSNs as against compared works

Comparative Analysis of Machine Learning and Deep Learning Models for Groundwater Potability Classification

Ensuring access to safe drinking water is a critical concern, particularly in regions with limited resources. This study evaluates groundwater potability using a range of machine learning models, including logistic regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest, as well as deep learning models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Feedforward Neural Networks (FNNs), and Long Short-Term Memory (LSTM). We collected thirty groundwater samples from residential and industrial locations in Jaen, Kano State, Nigeria, focusing on nine crucial physicochemical parameters: electric conductivity, pH, total dissolved solids, calcium, magnesium, chloride, zinc, manganese, and copper. Machine learning models, such as Logistic Regression and Random Forest, achieved accuracy scores of 0.833. They were closely followed by deep learning models, such as ANNs, with an accuracy score of 0.833, and LSTM, which scored 0.666. KNN and SVC provided moderately accurate predictions, scoring 0.667, while CNN and FNN achieved lower scores of 0.333 and 0.5, respectively. This study represents a significant step toward ensuring safe drinking water for communities and preserving the sustainability of natural resources

Design and construction of a microcontroller based single axis solar tracker

Solar energy is rapidly gaining popularity as an important means of expanding renewable energy resources. As such, it is vital that those in engineering fields understand the technologies associated with this area. This paper presents the design and construction of a microcontroller-based solar panel tracking system. Solar tracking allows more energy to be produced because the solar array is able to remain aligned to the sun. A working system will ultimately be demonstrated to validate the design. Problems and possible improvements will also be presented.Key Words: Solar, Tracking, Microcontroller, photocells, drivers, single axis