Obstacle Avoidance Scheme Based Elite Opposition Bat Algorithm for Unmanned Ground Vehicles

Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidance provided that the size of the obstacle does not exceed the size of the UGV. The EOBA based scheme when compared with BA and PSO schemes obtained an average percentage reduction of 21.82% in terms of path length and 60% in terms of time taken to reach the target destination. The uniqueness of this approach is that the UGV avoid collision with an obstacle at a distance of 0.3 m from nearby obstacles as against taking three steps backward before avoiding obstacl

CICM: A Collaborative Integrity Checking Blockchain Consensus Mechanism for Preserving the Originality of Data the Cloud for Forensic Investigation

The originality of data is very important for achieving correct results from forensic analysis of data for resolving the issue. Data may be analysed to resolve disputes or review issues by finding trends in the dataset that can give clues to the cause of the issue. Specially designed foolproof protection for data integrity is required for forensic purposes. Collaborative Integrity Checking Mechanism (CICM), for securing the chain-of-custody of data in a blockchain is proposed in this paper. Existing consensus mechanisms are fault-tolerant, allowing a threshold for faults. CICM avoids faults by using a transparent 100% agreement process for validating the originality of data in a blockchain. A group of agreement actors check and record the original status of data at its time of arrival. Acceptance is based on general agreement by all the participants in the consensus process. The solution was tested against practical byzantine fault tolerant (PBFT), Zyzzyva, and hybrid byzantine fault tolerant (hBFT) mechanisms for efficacy to yield correct results and operational performance costs. Binomial distribution was used to examine the CICM efficacy. CICM recorded zero probability of failure while the benchmarks recorded up to 8.44%. Throughput and latency were used to test its operational performance costs. The hBFT recorded the best performance among the benchmarks. CICM achieved 30.61% higher throughput and 21.47% lower latency than hBFT. In the robustness against faults tests, CICM performed better than hBFT with 16.5% higher throughput and 14.93% lower latency than the hBFT in the worst-case fault scenario

A Nonlinear Fuzzy Controller Design Using Lyapunov Functions for an Intelligent Greenhouse Management in Agriculture

The importance of agronomists in large-scale production of food crops under considerate environmental weather conditions cannot be overemphasized. However, emerging global warming is a threat to food security due to its effect on soil depletion and ecosystem degradation. In this work, the design of the proposed intelligent context is to observe, model and simulate greenhouse control system activity towards the management of the farm crop growth as the affected salient environmental parameters. Characteristically, temperature and humidity are the major factors that determine the crop yield in a greenhouse but the case of a dry air environment or beyond 300C−350C of high air humidity will affect crop growth and productivity. A Mamdani technique of fuzzy logic controller with non-linear consequent is used for intelligent greenhouse design in the LABVIEW virtual environment. This approach is used to mimic the human thought process in the system control by setting some logical rules that guide the greenhouse functions. For the system stabilization achievement, a direct method of Lyapunov functions was proposed. The simulation model result shows that, the average temperature of 18.50C and humidity 65% is achieved for a decent environment of crop growth and development during winter. However, the average temperature and humidity achieved during summer is 27.50C&70% respectively. For every season that is beyond 30.50Cand75% of temperature and humidity will require automation of roof opening and water spilled

A Specific Routing Protocol for Flying Adhoc Network

This paper presents a novel data and timed control routing protocol which is Flying Adhoc Network (FANET) specific. The developed FANET specific routing protocol laid emphasis on the route connectivity in the network by considering the captured data size, minimum allowable distance between randomly moving nodes and connection time. The performance of the proposed FANET specific routing protocol was simulated using NS3. The obtained throughput value for the routing protocol fluctuated between 742.064kbps and 755.083kbps as data are exchanged between nodes. This showed that when all the UAVs are on the network and communicating with one another, the throughput is flatline and not plummet. This implies consistency as nodes join and leave the network. The packet delivery ratio obtained for the FSRP during simulation was 96.13%. These results implied that data is successfully transmitted between the UAV acting as server and UAV acting as client on the network

Implementing Flash Event Discrimination in IP Traceback using Shark Smell Optimisation Algorithm

 Denial of service attack and its variants are the largest ravaging network problems. They are used to cause damage to network by disrupting its services in order to harm a business or organization. Flash event is a network phenomenon that causes surge in normal network flow due to sudden increase in number of network users, To curtail the menace of the Denial of service attack it is pertinent to expose the perpetrator and take appropriate action against it. Internet protocol traceback is a network forensic tool that is used to identify source of an Internet protocol packet. Most of presently available Internet protocol traceback tools that are based on bio-inspired algorithm employ flow-based search method for tracing source of a Denial of service attack without facility to differentiate flash event from the attack. Surge in network due to flash event can mislead such a traceback tool that uses flow-based search. This work present a solution that uses hop-by-hop search with an incorporated discrimination policy implemented by shark smell optimization algorithm to differentiate the attack traffic from other traffics. It was tested on performance and convergence against an existing bio-inspired traceback tool that uses flow-base method and yielded outstanding results in all the test

Modified Token Based Congestion Control Scheme for Opportunistic Networks

To address congestion issues in Opportunistic Networks (OppNets) a modified token-based congestion control with adaptive forwarding mechanism is proposed. The mechanism allows the network nodes holding a valid token to inject message into the network or other neighboring node. At the point of congestion, the algorithm has the potential to redirect the traffic from more congested node to congestion free node for the purpose of effective resource utilization and fairness in the network. Tokens are evenly distributed throughout the network. Using opportunistic network environment (ONE) simulator we illustrate the performance of modified token-based congestion control algorithm, which results in reduction for messages, and network transit time due to congestion across all the scenarios considered. At different queue sizes of (QS-10, QS-20, QS-30 and QS-40), modified token based congestion control algorithm has 13.91%, 10.71%, 5.46%, and 4.22% respectively reduction in dropped messages. In addition, at greatest connected component values of 50%, 60%, 70% and 80%, modified token-based congestion control has 8.34%, 2.19%, 4.61%, and 7.63% respectively decrease in network transit time. These results are substantial because they indicate a reduction in both network storage as well as time

Optimized Model Simulation of a Capacitated Vehicle Routing problem based on Firefly Algorithm

This paper presents an optimized solution to a capacitated vehicle routing (CVRP) model using firefly algorithm (FFA). The main objective of a CVRP is to obtain the minimum possible total travelled distance across a search space. The conventional model is a formal description involving mathematical equations formulated to simplify a more complex structure of logistic problems. These logistic problems are generalized as the vehicle routing problem (VRP). When the capacity of the vehicle is considered, the resulting formulation is termed the capacitated vehicle routing problem (CVRP). In a practical scenario, the complexity of CVRP increases when the number of pickup or drop-off points increase making it difficult to solve using exact methods. Thus, this paper employed the intelligent behavior of FFA for solving the CVRP model. Two instances of solid waste management and supply chain problems is used to evaluate the performance of the FFA approach. In comparison with particle swarm optimization and few other ascribed metaheuristic techniques for CVRP, results showed that this approach is very efficient in solving a CVRP model

Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework

Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on object-based machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively

Crypto Hash Algorithm-Based Blockchain Technology for Managing Decentralized Ledger Database in Oil and Gas Industry

This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes

Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework

Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on object-based machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively