Improved Hybrid Blind PAPR Reduction Algorithm for OFDM Systems

The ever growing demand for high data rate communication services resulted into the development of long-term evolution (LTE) technology. LTE uses orthogonal frequency division multiplexing (OFDM) as a transmission technology in its PHY layer for down-link (DL) communications. OFDM is spectrally efficient multicarrier modulation technique ideal for high data transmissions over highly time and frequency varying channels. However, the transmitted signal in OFDM can have high peak values in the time domain due to inverse fast Fourier transform (IFFT) operation. This creates high peak-to-average power ratio (PAPR) when compared to single carrier systems. PAPR drives the power amplifiers to saturation degrading its efficiency by consuming more power. In this paper a hybrid blind PAPR reduction algorithm for OFDM systems is proposed, which is a combination of distortion technique (Clipping) and distortionless technique (DFT spreading). The DFT spreading is done prior to clipping reducing significantly the probability of having higher peaks in the composite signal prior to transmission. Simulation results show that the proposed algorithm outperforms unprocessed conventional OFDM transmission by 9 dB. Comparison with existing blind algorithms shows 7 dB improvement at error rate 10–3 and 3 dB improvement at error rate 10–1 when operating in flat fading and doubly dispersive channels, respectively.Keywords:    LTE Systems; OFDM; Peak to Average Power Ratio; DFT spreading; Signal to Noise Power Ratio

Filtering Effect on RSSI-Based Indoor Localization Methods

Indoor positioning systems are used to locate and track objects in an indoor environment. Distance estimation is done using received signal strength indicator (RSSI) of radio frequency signals. However, RSSI is prone to noise and interference which can greatly affect the accuracy performance of the system. In this paper Internet of Things (IoT) technologies like low energy Bluetooth (BLE), WiFi, LoRaWAN and ZigBee are used to obtain indoor positioning. Adopting the existing trilateration and positioning algorithms, the Kalman, Fast Fourier Transform (FFT) and Particle filtering methods are employed to denoise the received RSSI signals to improve positioning accuracy. Experimental results show that choice of filtering method is of significance in improving the positioning accuracy. While FFT and Particle methods had no significant effect on the positioning accuracy, Kalman filter has proved to be the method of choice in for BLE, WiFi, LoRaWAN and ZigBee. Compared with unfiltered RSSI, results showed that accuracy was improved by 2% in BLE, 3% in WiFi, 22% in LoRaWAN and 17% in ZigBee technology for Kalman filtering method

Blind Algorithm Development for Peak to Average Power Ratio Reduction in OFDM Systems under Frequency Selective Channels

One major drawback of orthogonal frequency division multiplexing (OFDM) system is peak to average power ratio (PAPR). This effect causes high power amplifier (HPA) to introduce intermodulation and out of band radiation as the signal goes through, thus degrades the performance of OFDM systems. This paper proposes blind algorithms which takes advantage of signal transformation technique and signal distortion technique. Simulation results show that at complementary cumulative distribution function (CCDF) level of 10-3 , the proposed algorithm achieved 3.2 dB PAPR improvement compared to discrete Fourier transform with interleaved frequency division multiple access (DFT-IFDMA) based algorithm. The bit error rate (BER) performance has degraded by 2 dB compared to the original OFDM signal with no distortion under frequency selective channel (FCS) at BER of 10-4 . These presented results, mark this algorithm as a better candidate for PAPR reduction algorithm in long term evolution (LTE) network. Under AWGN channels, the proposed algorithm performs better both in low and high signal power values. Under frequency selective channels, the existing and proposed algorithm converges after 10 dB of signal to noise power values. The low BER transmissions at low signal power values signify energy efficiency, ideal for portable wireless devices with limited battery power

IoT-Based Smart Fishing Gear for Sustainability of the Tanzania Blue Economy

The fishing industry engages many Tanzanians and is among the leading sectors of Blue Economy in the country. However, fishing practices are small-scale with poor and insufficient number of fishing facilities, hence limiting productivity and efficacy. Studies argue that the low level of technology currently used in the country could possibly be an impeding factor. Specifically, fishers use non-interactive gears that cannot instantaneously update status and alert them whenever the gears are ready for collection. In such scenarios, fishers not only waste their time but also scarce resources such as fuel to facilitate trips to and from the fishing sites to check status of the gears. Thus, the application of Information and Communication Technology (ICT) could improve the production and lower operational costs is hypothesized. ICT solutions can help realize the processes with minimum human interventions while adding intelligence to the systems to make informed decisions and hence improve systems’ efficiency. In this work, an IoT based smart fishing gear that counts and records the number of fishes in the gear and then displays them on a mobile application is proposed. The system can send alerts to the user when the required number of fish is attained, and provides navigation support to localize the distant filled gears. Results show that the system can send the location of a given gear and timely update the number of catches via the mobile application

Resource Efficient Advanced Metering Infrastructure Model

Advanced Metering Infrastructure (AMI) enables two-way communication between smart devices and utility control centers. This involves remote monitoring and control of energy consumption as well as other parameters in the electrical power network in real time. However, increasing technologies in AMI due to huge deployment of smart meters, integration of devices and application of sensors, demand a strong architectural model with the best network topology to guarantee efficient usage of network resources with minimal latency. In this work, a resource efficient multi-hop network architecture is proposed using hybrid media access protocols. The architecture combines queuing and random-access protocol to achieve optimal network performance. Numerical results show that the probability of delay incurred by an arbitrary smart meter depends on the mean and distribution of the queue switch over a period. It is also observed that for a single queued system, the throughput performance is equal to the existing hybrid method. As the number of smart meters increases to 500, the throughput of the proposed method improves by 10% compared to the existing method. Likewise, as the number of smart meters increases to 500, the delay reduced by 15% compared to the existing method. Keywords: Advanced Metering Infrastructure; hybrid media access protocols; Smart Meter; Smart Grid; Power Network

Smart Electric Meter Deployment in Tanzania: A Survey

Using information and communication technologies (ICT) to make the electrical power network intelligent and smarter (smart grid) has been the focal point in transforming electrical power industry. The idea behind smart grid is to transform the Tanzanian power sector into a secure, adaptive, sustainable, and digitally enabled ecosystem that provides reliable and quality energy for all with active participation of stakeholders. Smart metering is a central segment in realizing smart grid. However, a big question is whether Tanzanian power stakeholders are ready for smart metering technology investments for household applications. Operation and maintenance of a smart metering solution is a relatively new business in Tanzania and requires investment in resources and capacity building. A case study was conducted at the utility company in Dar es Salaam offices, to investigate the deployment status and services offered. Fixed tariff rates, high cost, low rates on returns of investment and non-customization of the features, were some of the shortcomings identified by the study in terms of non-deployment in residential homes. Further, the authors, propose development of standardization document for smart metering technologies and the adoption of software based smart meter for residential applications using Internet of Things platform. Its low cost of development and ease installation would be ideal for residential applications. Keywords:  Smart grid, Utility Company, Smart meter, Advanced Metering Infrastructure, Deployment Status

Design and Implementation of Distributed Identity and Access Management Framework for Internet of Things (IoT) Enabled Distribution Automation

The smart grid and Internet of Things (IoT) technologies play vital roles in improving the quality of services offered in traditional electrical grid. They open a room for the introduction of new services like distribution automation (DA) that has a significant advantage to both utility companies and final consumers. DA integrates sensors, actuators, intelligent electrical devices (IED) and information and communication technologies to monitor and control electrical grid. However, the integration of these technologies poses security threats to the electrical grid like Denial of Service (DoS) attacks, false data injection attacks, and masquerading attacks like system node impersonation that can transmit wrong readings, resulting in false alarm reports and hence leading to incorrect node actuation. To overcome these challenges, researchers have proposed a centralized public key infrastructure (PKI) with bridged certificate authority (CA) which is prone to DoS attacks. Moreover, the proposed blockchain based distributed identity and access management (DIAM) in IoT domain at the global scale is adding communicational and computational overheads. Also. It is imposing new security threats to the DA system by integrating it with online services like IoTEX and IoTA. For those reasons, this study proposes a DIAM security scheme to secure IoT-enabled distribution automation. The scheme divides areas into clusters and each cluster has a device registry and a registry controller. The registry controller is a command line tool to access and manage a device registry. The results show that the scheme can prevent impersonated and non-legitimate system nodes and users from accessing the system by imposing role-based access control (RBAC) at the cluster level. Keywords: Distributed Identity and Access Management; Electrical Secondary Distribution Network; Internet of Things; IoT Enabled Distribution Automation; Smart Grid Securit

Another look at the Frame shapes of finite groups

Part 4: Local Technical PapersInternational audienceTo increase network capacity of a mobile communication system, three main ways can be derived from Claude Shannon equation. These are the use of smaller cells, increase of bandwidth and improvement of communication technology. Since there is only a finite amount of radio spectrum available and it is also required by other applications, there are limits to bandwidth increment. Combining the later and former, LTE small cells method is best practical way to increase system capacity of mobile communication system. However, deployment of femtocells with small coverage range leads to frequent handover initiation. The problem escalates when femtocells operate in open access mode while accommodating highly mobile users who initiate unnecessary handovers as they stay in a femtocell for short time. To tackle these challenges, a hybrid handover decision algorithm is presented. The proposed algorithm selects the most appropriate target femtocell for handover using velocity of the user, throughput gain and adaptation of signal averaging and hysteresis margin methods. Simulation results show 1.7 times reduction in the amount of handovers in comparison with the traditional handover schemes

Robust Trilateration Based Algorithm for Indoor Positioning Systems

Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using radio signal identification was developed. The algorithm uses circle expansion and reduction mechanism to achieve better RSSI-Distance relationship. The distances from RSSI-Distance relationship are used in trilateration algorithm for position estimation. Experiments were performed to compare position accuracy of the basic RSSI-Based and the proposed algorithm. Simulation results showed that proposed algorithm showed less average positioning errors by 11.2066% and 3.7279% at path loss coefficients of 3.11 and 3.21, respectively compared to the existing algorithms. Likewise, the proposed algorithm showed 2.7282% increase in positioning error when environment was changed from that of path loss coefficient 3.11 to 3.21. The existing basic algorithms show error fluctuation of 10% with the same environment changes. Keywords: Indoor Positioning System; RFID; RSSI; Trilateratio

Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks

Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of  96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors. Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution networ