Received Data Packets Research Articles

Effect of NTT on Performance of AODV in a Linear AD HOC Network

Wireless Ad Hoc Networks (WANETs) are predicted to be prevalent networks in coming future because of their infrastructure-less characteristics that result in self-organization, self-healing, and self-configuration of such networks. Routing means the process of selecting a path along which data can be transferred from source to destination that plays a significant role in the performance of ad hoc networks. There are many routing protocols developed for wireless ad hoc networks. One of such protocols, developed by C. E. Perkins, is Ad Hoc On-demand Distance Vector (AODV) routing protocol, which is a reactive routing protocol, AODV is used to establish route(s) between source node(s) and destination node(s) as and when routes are required. Performance of AODV routing protocol depends heavily on choice of values of a set of predefined attributes. One of such attributes of AODV is Node Traversal Time (NTT) which is supposed to play significant role in establishing route(s) between source node(s) and destination node(s). In this paper, we have investigated the performance of AODV in a linear wireless ad hoc network using different values of NTT using as well as without using Hello messages. The performance of the network has been measured using several metrics like Route Error Overhead, Routing Overhead, Normalized Routing Load, Average Hop Count, Total Number of Received Data Packets, Packet Delivery Ratio, Average End-to-End Delay and Throughput. It is observed that choice of value of NTT has a significant effect on establishment of route(s) between source node(s) and destination node(s) that eventually affects the performance of the linear wireless ad hoc network under consideration. The insights gained through this work could be useful in analysis of more realistic and complex networks.

Age of Information Aware UAV Deployment for Intelligent Transportation Systems

The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.

Implementation of a LoRa Mesh Library

LoRa is a popular communication technology in the Internet of Things (IoT) domain, providing low-power and long-range communications. Most LoRa IoT applications use the LoRaWAN architecture, which builds a star topology between LoRa end nodes and the gateway they connect to. However, LoRa can also be used for the communication between end nodes themselves, forming a mesh network topology. In this paper, we present a library that allows to integrate LoRa end nodes into a LoRa mesh network, in which a routing protocol is used. Thus, an IoT application running on these nodes can use the library to send and receive data packets to and from other nodes in the LoRa mesh network. The designed routing protocol is proactive, and maintains the routing table at each node updated by sending routing messages between neighboring nodes. The implemented library has been tested on embedded boards featuring an ESP32 microcontroller and a LoRa single-channel radio. By using our LoRa mesh library, nodes do not need to connect to a LoRaWAN gateway, but among themselves. This opens the possibility for new, distributed applications solely built upon tiny IoT nodes.

A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels.

On the secrecy outage analysis of underlay cognitive radio systems with buffer‐aided relaying under Nakagami‐ m channels

In this work, the physical layer security performance of an underlay cognitive buffer-aided relay network over the general Nakagami-m fading channel model is addressed, where a primary source communicates with a destination via a decode-and-forward relay node in the presence of an eavesdropper and a secondary communication system. The relay is equipped with a finite-size storage equipment or buffer which can store the sequence of received data packets from source and forward it immediately or subsequently in an adequate time-slot. In this way, the buffer relay enables to adopt a link selection scheme in order to detect the best available link, (i.e. the one with higher secrecy rate) among the available data at source or at the relay buffer. The closed-form expression for the secrecy outage probability (SOP), which is a widely adopted performance metric in the considered scenario, is also derived. Numerical results confirm the accuracy of the derived closed-form analytic expression for SOP and indicates the interaction between secrecy performance and the Nakagami channel parameter m. In particular, it is shown that the SOP experiences a significant drop as the Nakagami channel parameter increases for a predefined target secrecy rate.

Scheduling Design and Performance Analysis of Carrier Aggregation in Satellite Communication Systems

Carrier Aggregation is one of the vital approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in cellular networks, its application to satellite systems has not been thoroughly explored yet. Carrier aggregation can offer an enhanced and more consistent quality of service for users throughout the satellite coverage via combining multiple carriers, utilizing the unused capacity at other carriers, and enabling effective interference management. Furthermore, carrier aggregation can be a prominent solution to address the issue of the spatially heterogeneous satellite traffic demand. This paper investigates introducing carrier aggregation to satellite systems from a link layer perspective. Deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. To this end, a novel load balancing scheduling algorithm has been proposed to distribute data packets across the aggregated carriers based on channel capacities and to utilize spectrum efficiently. Moreover, in order to ensure that the received data packets are delivered without perturbing the original transmission order, a perceptive scheduling algorithm has been developed that takes into consideration channel properties along with the instantaneous available resources at the aggregated carriers. The proposed modifications have been carefully designed to make carrier aggregation transparent above the medium access control (MAC) layer. Additionally, the complexity analysis of the proposed algorithms has been conducted in terms of the computational loads. Simulation results are provided to validate our analysis, demonstrate the design tradeoffs, and to highlight the potentials of carrier aggregation applied to satellite communication systems.

Packet Traffic Arbiter Interface for Dual-Mode Bluetooth Controller

Wireless devices sharing the same environment and operating in the same ISM frequency bands are likely to conflict with one another. Interference and cross talk between the devices are unavoidable due to the growing prevalence of wireless technologies. This paper explains the implementation of the Packet Traffic Arbiter interface that organizes the wireless packet traffic for the Dual-Mode Bluetooth controller. The main objective of the Dual-Mode Packet Traffic Arbiter is to provide access to the Dual-Mode modem and RFIC whenever Bluetooth low energy or Bluetooth classic wants to transmit or receive data packets. The arbitration feature helps in lowering the interference between the two-Bluetooth devices, provides coexistence feature and improves the overall throughput of the Dual-Mode Bluetooth.

A two‐level clustering mechanism for energy enhancement in Internet‐of‐Things‐based wireless sensor networks

SummaryWireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms.

A mathematical framework for link failure time estimation in MANETs

Mobile ad-hoc network (MANET) is an infrastructure-less network of mobile nodes, connected by wireless links. Although mobility is the key characteristic of MANETs, yet the frequent movement of nodes may lead to link failure. This frequent topology change due to evasive mobility is one of the primary reason of Quality of service (QoS) degradation in conventional MANETs. If link failure time of already established root is estimated in advance, data packet loss can be avoided by exploring alternate path well within time. Thus, accurate link failure time estimation is one of the key areas that have drawn the attention of researchers of the ad-hoc networking community. This paper outlines a least-square quadratic polynomial regression-based technique for estimating link failure time by using the signal strength of received data packets. The distance between the transmitting and receiving nodes is calculated from the received signal strength of data packets. The authors have generated an optimal error quadratic model between this node distance and data packet arrival time. After quadratic model formulation, the link failure time is estimated by finding the moment at which the maximum communication range will be achieved for considered transmitter–receiver pair. A detailed analysis has been done for observing the behaviour of link failure time estimation accuracy of the proposed technique with varying network parameters. The repercussion of mobility variation on estimated link failure time has also been analysed. The proposed technique has also been compared with an existing interpolation-based technique. Network Simulator 2.35 has been selected as the primary simulation tool along with BonnMotion and MOVE tool for various mobility scenarios generation.

Towards Optimized One-Step Clustering Approach in Wireless Sensor Networks

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-heads selection and distribution, are considered implicitly in the model. Second, an innovative fitness function that directly maximizes the WSN lifetime is proposed. Finally, a global optimum of the whole network lifespan clustering schemes is targeted. The proposed model is then solved by a particle swarm optimization meta-heuristic based algorithm. This algorithm follows a centralized one-step off-line approach, in which the clustering schemes of the whole network lifetime and their associated durations are computed at the initialization of the network operations. The one-step approach is enabled by an energy prediction mechanism that allows to reduce the costs of the periodic network re-clustering. Simulation results show clear enhancement in network lifespan and number of received data packets as compared to some state-of-the-art clustering approaches.

Investigating Master-Slave Architecture for Underwater Wireless Sensor Network.

A significant increase has been observed in the use of Underwater Wireless Sensor Networks (UWSNs) over the last few decades. However, there exist several associated challenges with UWSNs, mainly due to the nodes’ mobility, increased propagation delay, limited bandwidth, packet duplication, void holes, and Doppler/multi-path effects. To address these challenges, we propose a protocol named “An Efficient Routing Protocol based on Master–Slave Architecture for Underwater Wireless Sensor Network (ERPMSA-UWSN)” that significantly contributes to optimizing energy consumption and data packet’s long-term survival. We adopt an innovative approach based on the master–slave architecture, which results in limiting the forwarders of the data packet by restricting the transmission through master nodes only. In this protocol, we suppress nodes from data packet reception except the master nodes. We perform extensive simulation and demonstrate that our proposed protocol is delay-tolerant and energy-efficient. We achieve an improvement of 13% on energy tax and 4.8% on Packet Delivery Ratio (PDR), over the state-of-the-art protocol.

DATA TRAFFIC TRUST MODEL FOR CLUSTERED WIRELESS SENSOR NETWORK

Trust is an essential parameter among sensor nodes to provide secured and successful communication. Many trust management schemes have been proposed earlier for large scale Wireless Sensor Network (WSN) however not cooperates well in terms of low dependability, memory overheads, large communication etc, therefore a system called Data Traffic Trust Scheme (DTTS) for clustered WSN is proposed here. Here the trust nodes are identified through the data traffic sampling rate. The trust rate is identified through the number of sent and receive data packets and the malicious packets are diagnosed through the un-matching packet rate. The simulation results are evaluated to show the efficiency for the proposed scheme.

Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm‐Based Feature Selection and Fast‐Learning Network

Given the growth of wireless networks and the increase of the advantages and applications of communication networks, especially mobile ad hoc networks (MANETs), this type of network has attracted the attention of users and researchers more than before. The benefit of these types of networks in various kinds of networks and environments is that MANET does not require to hardware infrastructure to communicate and send and receive data packets within the network. It is one of the main reasons for using these MANET in various fields. On the other hand, the increased popularity of these networks has led to many challenges, one of the most important of which is network security. In this regard, a lack of regulatory and security infrastructure in MANETs has caused some problems in sending and receiving data, where intrusion in the network has been recognized as one of the most important issues. In MANETs, wireless notes act as a link between the source and destination nodes and play the role of relays and routers in the network. Therefore, malicious node penetration and the destruction of information packages become feasible. Today, intrusion detection systems (IDSs) are used as a solution to deal with the problem through remote monitoring of the performance and behaviors of nodes existing in wireless sensor networks. In addition to detecting malicious nodes in the network, IDSs can predict the behavior of malicious nodes in the future in most cases. Therefore, the present study introduced a network IDS (NIDS) entitled MOPSO‐FLN by using a combination of multiobjective particle swarm optimization algorithm‐ (MOPSO‐) based feature subset selection (FSS) and fast‐learning network (FLN). In this work, we used the KDD Cup99 and dataset to select features, train the network, and test the model. According to the simulation results, this method was able to improve the performance of the IDS in terms of evaluation criteria, compared to other previous methods, by creating a balance between the objectives of the number of representative features and training errors based on the evolutionary power of MOPSO.

Clock synchronization error compensation algorithm based on BP neural network model

Error compensation is an important guarantee method to ensure the accuracy of clock synchronization in underwater sensor networks. Existing research methods mainly use linear fitting and least square method to compensate for clock synchronization parameters. Underwater wireless sensor network nodes are mobile, which leads the network nodes to be always in a time-varying state. In the process of synchronous forwarding, the position where the node sends and receives data packets will change, resulting in a relative moving distance, leading the dynamic delay to an increase in. In this way, as the number of forwarding nodes increases, the error of the clock gradually increases, causing the synchronization accuracy of the underwater sensor wireless network to gradually decrease. The existing underwater wireless sensor network clock synchronization algorithm does not fully consider the dynamic time delay caused by the movement of the node with the ocean current. It only uses the time stamp mechanism to solve the clock synchronization parameters, and then uses the traditional linear fitting to refine the synchronization parameters. The accurate solution of dynamic time delay is a key factor of synchronization accuracy. The use of traditional optimization algorithms to refine the synchronization parameters can easily fall into a local optimum, which makes the synchronization accuracy not high. Therefore, the existing traditional research on clock synchronization algorithms cannot well solve the problem of clock synchronization accuracy caused by node mobility. However this type of method does not consider the clock synchronization accuracy of node movement affected by ocean currents. To solve this problem, this paper proposes a clock synchronization error compensation algorithm based on BP neural network model. First, the deep-sea Lagrangian ocean current model is used to describe the movement of underwater nodes and simulate the movement speed of underwater nodes, and then a clock synchronization parameter model is established, and finally a BP neural network clock synchronization error compensation model is build, which conforms to the underwater environment, and the excitation function is defined, and regular term factor and compensatory factor are introduced to avoid model over-fitting. The BP neural network model clock synchronization error compensation algorithm is established for error back propagation. Simulation experiments show that compared with the comparison algorithm TSHL, MM-sync, and MU-sync, the accuracy of clock synchronization, namely the error between clock synchronization time and standard time, increased by 37.42%, 17.29% and 21.86%, and the mean square error is significantly reduced.

Detection and Prevention of Blackhole Attack in the AOMDV Routing Protocol

Mobile ad-hoc network is a collection of dynamically organized nodes where each node acts as a host and router. Mobile ad-hoc networks are characterized by the lack of preexisting infrastructures or centralized administration. So, they are vulnerable to several types of attacks, especially the Blackhole attack. This attack is one of the most serious attacks in this kind of mobile networks. In this type of attack, the malicious node sends a false answer indicating that it has the shortest path to the destination node by increasing the sequence number and decreasing the number of hops. This will have a significant negative impact on source nodes which send their data packets through the malicious node to the destination. This malicious node drop received data packets and absorbs all network traffic. In order overcome this problem, securing routing protocols become a very important requirement in mobile ad-hoc networks. Multipath routing protocols are among the protocols affected by the Blackhole attack. In this paper, we propose an effective and efficient technique that avoids misbehavior of Blackhole nodes and facilitates the discovery for the most reliable paths for the secure transmission of data packets between communicating nodes in the well-known Ad hoc On-demand multi-path routing protocol (AOMDV). We implement and simulate our proposed technique using the ns 2.35 simulator. We also compared on how the three routing protocols AOMDV, AOMDV under Blackhole attack (BHAOMDV), and the proposed solution to counter the Blackhole attack (IDSAOMDV) performs. The results show the degradation on how AOMDV under attack performs, it also presents similarities between normal AOMDV and the proposed solution by isolating misbehaving node which has resulted in increase the performance metrics to the standard values of the AOMDV protocol.

EEARP - an Efficient and Energy Aware Routing Protocol for Underwater Wireless Sensor Networks

Recently, Underwater Wireless Sensor Networks (UWSNs) have attracted significant attention from both academia and industry to explore the vast underwater environment. Since UWSNs suffer from long propagation delay, low bandwidth, and high error rate, providing an efficient routing protocol is challenging. This paper proposes EEARP, a series of new routing protocols for underwater networks to improve the performance of existing DBR. The DBR does not consider energy. It only receives data packets from upper depth node and forwards the packets to lower depth nodes. Moreover, this greedy behavior of DBR causes void area problem. EEARP creates a directed acyclic graph rooted at a sink. Each node receives information like depth, energy, and the number of parents from its parents. When forwarding, each node sends a data packet to one or more of its parent nodes. We implemented EEARP in NS2 simulator and evaluated its performance under different scenarios. Results confirm that EEARP outperforms DBR in terms of energy saving, network lifetime, end-to-end delay and packet loss ratio.

Efficient Mechanism for Data Gathering and Transmission in Multihop Wireless Sensor Networks

In this paper, we propose an efficient mechanism for data gathering and transmission in multi-hop wireless sensor networks. In wireless sensor networks, data packets are transferred to the sink node. So many packets are gathered around the sink, resulting in significant delay and collision. To solve this problem, the proposed mechanism used a two-way method; one method works between 1-hop nodes from the sink node and the sink node. In this method, the data packet is transmitted in predefined time to reduce collision and delay. The other method works between nodes except the sink node. In this method, receiver nodes use BRN (backup receiver node) mechanism to reduce energy consumption, thereby increasing the lifetime of the entire networks. In the BRN mechanism, a receiver node that has received a data packet once does not participate in receiving data packets twice in succession by changing its status value. The reason that the receiver node participating in the data packet transmission once does not participate in receiving the second consecutive data packet is to solve the hot spot problem in which two receiver nodes alternately receive the data packet. Also, we propose an enhanced method for data gathering. The proposed method is used for the intensity and direction of the signal in order to increase the reliability of the duplicate data packet. Our simulation and numerical analysis results show that our mechanism outperforms X-MAC in terms of transmission delay and energy consumption.

A Parallelization based Data Management Framework for Pervasive IoT Applications

Pervasive Internet of Things (IoT) is a research paradigm that has attracted considerable attention nowadays. The main aim of pervasive IoT is that in the future, the everyday objects (devices) would be accessible, sensed, and interconnected inside the global structure of the Internet. But in most of the pervasive IoT applications, the resources of an IoT device such as storage, processing, and energy are limited; as such there is a need for management of resources in such applications. Multiple aspects related to the data such as the type of data, size of data, number of transmission and reception of data packets, the structure of data, etc are taken into consideration while managing the resources of pervasive IoT applications. Therefore data management is essential for the management of limited resources in such applications. This paper presents the recent studies and related information in data management for pervasive IoT applications having limited resources. This paper also proposes a parallelization based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach.

UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.