Enhanced Home Energy Management Scheme (EHEM) in Smart Grids

Wireless Sensor Networks (WSNs) have become one of the most important components that play a major role in home environment applications. It plays a major role in the creation and the development of smart home environments. Smart homes creates home area network (HAN) to be used in different applications including smart grids. In this paper, we propose an enhancement to in-Home Energy Management (iHEM) scheme, namely EHEM, to reduce energy consumption by shifting the residents’ demands to mid-peak or off-peak periods depending on the appliances priorities and delays. The proposed system handles challenging cases by using internal storage battery. The performance of the proposed system is compared against iHEM and the traditional iHEM scheme, based on the total cost of the power consumption. Obtained results show slight improvement over the existing iHEM schem

On the performance of probabilistic flooding in wireless mobile ad hoc networks

Broadcasting in MANET’s has traditionally been based on flooding, but this can induce broadcast storms that severely degrade network performance due to redundant retransmission, collision and contention. Probabilistic flooding, where a node rebroadcasts a newly arrived one-to-all packet with some probability, p, was an early suggestion to reduce the broadcast storm problem. The first part of this thesis investigates the effects on the performance of probabilistic flooding of a number of important MANET parameters, including node speed, traffic load and node density. It transpires that these parameters have a critical impact both on reachability and on the number of so-called “saved rebroadcast packets” achieved. For instance, across a range of rebroadcast probability values, as network density increases from 25 to 100 nodes, reachability achieved by probabilistic flooding increases from 85% to 100%. Moreover, as node speed increases from 2 to 20 m/sec, reachability increases from 90% to 100%. The second part of this thesis proposes two new probabilistic algorithms that dynamically adjust the rebroadcasting probability contingent on node distribution using only one-hop neighbourhood information, without requiring any assistance of distance measurements or location-determination devices. The performance of the new algorithm is assessed and compared to blind flooding as well as the fixed probabilistic approach. It is demonstrated that the new algorithms have superior performance characteristics in terms of both reachability and saved rebroadcasts. For instance, the suggested algorithms can improve saved rebroadcasts by up to 70% and 47% compared to blind and fixed probabilistic flooding, respectively, even under conditions of high node mobility and high network density without degrading reachability. The final part of the thesis assesses the impact of probabilistic flooding on the performance of routing protocols in MANETs. Our performance results indicate that using our new probabilistic flooding algorithms during route discovery enables AODV to achieve a higher delivery ratio of data packets while keeping a lower routing overhead compared to using blind and fixed probabilistic flooding. For instance, the packet delivery ratio using our algorithm is improved by up to 19% and 12% compared to using blind and fixed probabilistic flooding, respectively. This performance advantage is achieved with a routing overhead that is lower by up to 28% and 19% than in fixed probabilistic and blind flooding, respectively

A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices

Wireless traffic that is destined for a certain device in a network, can be exploited in order to minimize the availability and delay trade-offs, and mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be node-centric by considering the traversed nodal traffic in order to prolong the network lifetime. This work describes a quantitative traffic-based approach where a clustered Sleep-Proxy mechanism takes place in order to enable each node to sleep according to the time duration of the active traffic that each node expects and experiences. Sleep-proxies within the clusters are created according to pairwise active-time comparison, where each node expects during the active periods, a requested traffic. For resource availability and recovery purposes, the caching mechanism takes place in case where the node for which the traffic is destined is not available. The proposed scheme uses Role-based nodes which are assigned to manipulate the traffic in a cluster, through the time-oriented backward difference traffic evaluation scheme. Simulation study is carried out for the proposed backward estimation scheme and the effectiveness of the end-to-end EC mechanism taking into account a number of metrics and measures for the effects while incrementing the sleep time duration under the proposed framework. Comparative simulation results show that the proposed scheme could be applied to infrastructure-less systems, providing energy-efficient resource exchange with significant minimization in the power consumption of each device.Comment: 6 pages, 8 figures, To appear in the proceedings of IEEE 14th International Conference on High Performance Computing and Communications (HPCC-2012) of the Third International Workshop on Wireless Networks and Multimedia (WNM-2012), 25-27 June 2012, Liverpool, U

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

© 2017 Elsevier B.V. Efficiently detecting network intrusions requires the gathering of sensitive information. This means that one has to collect large amounts of network transactions including high details of recent network transactions. Assessments based on meta-heuristic anomaly are important in the intrusion related network transaction data\u27s exploratory analysis. These assessments are needed to make and deliver predictions related to the intrusion possibility based on the available attribute details that are involved in the network transaction. We were able to utilize the NSL-KDD data set, the binary and multiclass problem with a 20% testing dataset. This paper develops a new hybrid model that can be used to estimate the intrusion scope threshold degree based on the network transaction data\u27s optimal features that were made available for training. The experimental results revealed that the hybrid approach had a significant effect on the minimisation of the computational and time complexity involved when determining the feature association impact scale. The accuracy of the proposed model was measured as 99.81% and 98.56% for the binary class and multiclass NSL-KDD data sets, respectively. However, there are issues with obtaining high false and low false negative rates. A hybrid approach with two main parts is proposed to address these issues. First, data needs to be filtered using the Vote algorithm with Information Gain that combines the probability distributions of these base learners in order to select the important features that positively affect the accuracy of the proposed model. Next, the hybrid algorithm consists of following classifiers: J48, Meta Pagging, RandomTree, REPTree, AdaBoostM1, DecisionStump and NaiveBayes. Based on the results obtained using the proposed model, we observe improved accuracy, high false negative rate, and low false positive rule

Blockchain based voting system for Jordan parliament elections

Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters

Intelligent black hole detection in mobile AdHoc networks

Security is a critical and challenging issue in MANET due to its open-nature characteristics such as: mobility, wireless communications, self-organizing and dynamic topology. MANETs are commonly the target of black hole attacks. These are launched by malicious nodes that join the network to sabotage and drain it of its resources. Black hole nodes intercept exchanged data packets and simply drop them. The black hole node uses vulnerabilities in the routing protocol of MANETS to declare itself as the closest relay node to any destination. This work proposed two detection protocols based on the collected dataset, namely: the BDD-AODV and Hybrid protocols. Both protocols were built on top of the original AODV. The BDD-AODV protocol depends on the features collected for the prevention and detection of black hole attack techniques. On the other hand, the Hybrid protocol is a combination of both the MI-AODV and the proposed BDD-AODV protocols. Extensive simulation experiments were conducted to evaluate the performance of the proposed algorithms. Simulation results show that the proposed protocols improved the detection and prevention of black hole nodes, and hence, the network achieved a higher packet delivery ratio, lower dropped packets ratio, and lower overhead. However, this improvement led to a slight increase in the end-to-end delay

A performance comparison of smart probabilistic broadcasting of ad hoc distance vector (AODV).

Broadcast is a common operation used in Mobile Ad hoc Networks (MANETs) for many services, such as, routdiscovery and sending an information messages. The direct method to perform broadcast is simple flooding, which itcan dramatically affect the performance of MANET. Recently, a probabilistic approach to flooding has beenproposed as one of most important suggested solutions to solve the broadcast storm problem, which leads to thecollision, contention and duplicated messages. This paper proposed new probabilistic method to improve theperformance of existing on-demand routing protocol by reduced the RREQ overhead during rout discoveryoperation. The simulation results show that the combination of AODV and a suitable probabilistic rout discoverycan reduce the average end- to- end delay as well as overhead and still achieving low normalized routing load,comparing with AODV which used fixed probability and blind floodin

Advanced security testing using a cyber-attack forecasting model: A case study of financial institutions

As the number of cyber-attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber-security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network-based cyber-attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber-attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber-crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.Dado que el número de ciberataques a instituciones financieras ha aumentado en los últimos años, es esencial contar con un sistema avanzado que sea capaz de predecir el objetivo de un ataque. Un sistema de este tipo debe integrarse en los sistemas de detección existentes de las instituciones financieras, ya que les proporciona controles proactivos con los que detener un ataque mediante la predicción de patrones. Los sistemas de predicción avanzados también mejoran el diseño de software y las pruebas de seguridad de nuevas medidas avanzadas de ciberseguridad al proporcionar nuevos escenarios de prueba respaldados por la previsión de ataques. Este presente estudio desarrolló un modelo que pronostica futuros ciberataques basados ​​en redes contra instituciones financieras utilizando una red neuronal profunda. El conjunto de datos que se utilizó para entrenar y probar el modelo consistió en algunos de los mayores ataques cibernéticos a instituciones bancarias en los últimos tres años. Esto proporcionó información sobre nuevos patrones que pueden terminar en un delito cibernético. Estos nuevos ataques también fueron evaluados para determinar similitudes de comportamiento con el ataque conocido más cercano o una combinación de varios ataques existentes. Luego se evaluó el desempeño del modelo de pronóstico en un entorno bancario real y proporcionó una precisión de pronóstico del 90,36%. Como tal, las instituciones financieras pueden utilizar el modelo de pronóstico propuesto para mejorar sus medidas de prueba de seguridad.2022-2

Improvement on the Lifetime of the WSN Using Energy Efficiency Saving of Leach Protocol (New Improved LEACH)

Energy efficiency is an important aspect in wireless sensor networks. WSNs support a clustering-based routing protocol called Lower Energy Adaptive Clustering Hierarchy (LEACH) Protocol which uses hierarchical clustering to solve the energy consumption issue. The New Improved LEACH protocol combines LEACH and MD protocols. It allows the clusterhead (CH) to be in a sleep mode if there is no data to be sent, this is a contrast to the LEACH protocol which assumes that the CH is always switched on. In previous work, this approach was evaluated using mathematical model. The proposed protocol uses simulation model. The simulation tool we used for the purpose is MATLAB (version 7.10). Simulation results show that the New Improved LEACH routing protocol reduces energy consumption and increases the total lifetime of the WSN compared to the LEACH protocol