Search results

Ad hoc mobile networks are commonplace in every aspect of our everyday life. They become essential in many industries and have uses in logistics, science and the military. However, because they operate mostly in open spaces, they are exposed to a variety of dangers. The purpose of this study is to introduce a novel method for detecting the MAC layer misbehavior.

The proposed novel approach is based on exponential smoothing for throughput prediction to address this MAC layer misbehavior. The real and expected throughput are processed using an exponential smoothing algorithm to identify this attack, and if these metrics exhibit a trending pattern, an alarm is then sent.

The effect of the IEEE 802.11 MAC layer misbehavior on throughput was examined using the NS-2 network simulator, as well as the approval of our novel strategy. The authors have found that a smoothing factor value that is near to 0 provides a very accurate throughput forecast that takes into consideration the recent history of the updated values of the real value. As for the smoothing factor values that are near to 1, they are used to identify MAC layer misbehavior.

According to the authors’ modest knowledge, this new scheme has not been proposed in the state of the art for the detection of greedy behavior in mobile ad hoc networks.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

In vehicular ad hoc networks (VANETs), the information transmitted is broadcast in a free access environment. Therefore, VANETs are vulnerable against attacks that can directly perturb the performance of the networks and then provoke big fall of capability. Black hole attack is an example such attack, where the attacker node pretends that having the shortest path to the destination node and then drops the packets. This paper aims to present a new method to detect the black hole attack in real-time in a VANET network.

This method is based on capability indicators that are widely used in industrial production processes. If the different capability indicators are greater than 1.33 and the stability ratio (S_r) is greater than 75%, the network is stable and the vehicles are communicating in an environment without the black hole attack. When the malicious nodes representing the black hole attacks are activated one by one, the fall of capability becomes more visible and the network is unstable, out of control and unmanaged, due to the presence of the attacks. The simulations were conducted using NS-3 for the network simulation and simulation of urban mobility for generating the mobility model.

The proposed mechanism does not impose significant overheads or extensive modifications in the standard Institute of Electrical and Electronics Engineers 802.11p or in the routing protocols. In addition, it can be implemented at any receiving node which allows identifying malicious nodes in real-time. The simulation results demonstrated the effectiveness of proposed scheme to detect the impact of the attack very early, especially with the use of the short-term capability indicators (Cp, Cpk and Cpm) of each performance metrics (throughput and packet loss ratio), which are more efficient at detecting quickly and very early the small deviations over a very short time. This study also calculated another indicator of network stability which is S_r, which allows to make a final decision if the network is under control and that the vehicles are communicating in an environment without the black hole attack.

According to the best of the authors’ knowledge, the method, using capability indicators for detecting the black hole attack in VANETs, has not been presented previously in the literature.

The potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.

The black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.

Through the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.

All things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

The advent of Industry 4.0 (I4.0) and the requirements imposed on companies still need to be clarified. Companies still strive to understand I4.0 requirements and technological, organizational, operational and management challenges. Current literature on I4.0 underlies the importance of a roadmap with structured steps to achieve the benefits of I4.0, mainly focused on augmenting operational performance. Therefore, this paper proposes a roadmap to implement I4.0 focused on operational management concepts, mainly aiming to augment operational performance and bridge the gap between theory and practice regarding roadmaps focused on the operational management dimension.

This paper follows a research approach divided into the following stages: a literature review to analyze the I4.0 roadmaps and identify the main components of I4.0; development of the proposed I4.0 roadmap presented; field research to test the roadmap by collecting data from a manufacturing company in the automotive industry; validation of the roadmap through modeling and simulation.

The authors presented a production line design with real-time control, fast response, shop floor coordination and predictive capacity. The results prove that the proposed I4.0 roadmap augments operation performance in the investigated automotive company. The main results were work in process reduction, lead time reduction, output increase, real-time control, shop floor coordination and fast response.

The main novelty of the proposed roadmap is to move toward I4.0 implementation with a focus on the operational management dimension. The roadmap has an innovative combination of the two approaches – lean manufacturing and factory physics – a straightforward roadmap with only three steps: (1) requirements, (2) real-time control and (3) predictive capacity, a structured definition of the approaches and operational management concepts fundamental in each step.

Regarding the retail internal supply chain (SC), both retailers and research are currently focused on reactive food waste reduction options in stores (e.g. discounting or donations). These options reduce waste after a surplus has emerged but do not prevent an emerging surplus in the first place. This paper aims to reveal how retailers can proactively prevent waste along the SC and why the options identified are impactful but, at the same time, often complex to implement.

The authors follow an exploratory approach for a nascent topic to obtain insights into measures taken in practice. Interviews with experts from retail build the main data source.

The authors identify and analyze 21 inbound, warehousing, distribution and store-related options applied in grocery retail. Despite the expected high overall impact on waste, prevention measures in inbound logistics and distribution and warehousing have not been intensively applied to date.

The authors provide a structured approach to mitigate waste within retailers' operations and categorize the types of barriers that need to be addressed.

This research provides a better understanding of prevention options in retail operations, which has not yet been empirically explored. Furthermore, this study conceptualizes prevention and reduction options and reveals implementation patterns.