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Data Analysis and Prediction for WSN Based on Linear and Quadratic Optimization Techniques
Published in Santosh Kumar Das, Massimiliano Giacalone, Fuzzy Optimization Techniques in the Areas of Science and Management, 2023
Manoj Kumar Mandal, Arun Prasad Burnwal, B. K. Mahatha, Abhishek Kumar
The wireless sensor network (WSN) is made up of sensor nodes and base stations (BS). The sensor nodes’ role is to detect environmental information and transmit it to the BS. This information is processed by the BS, which predicts the user inquiry and responds appropriately [1-2]. The WSN's key parameter is energy, which efficiently reflects on other parameters. Because each sensor node has a limited battery capacity, this low energy capacity is insufficient for each user's activity. Some work is based on the different challenges of management systems for security issues of the information system within the context of the wireless network. Some of the operation is based on coverage optimization and metaheuristic optimization that helps model several applications [3-4]. During the operation, sometimes nodes fail to transmit the data packet, or the path between nodes fails due to a lack of required energy. This issue affects other network parameters efficiently, like increase in some network metrics, such as packet delivery ratio, throughput, and goodput, and decreases in some network metrics, such as packet loss, overhead, and end-to-end delay. The combining variation of both types of network parameters degrades the network lifetime and affects the overall operation of the network.
Design Principles and Privacy in Cloud Computing
Published in Gautam Kumar, Dinesh Kumar Saini, Nguyen Ha Huy Cuong, Cyber Defense Mechanisms, 2020
Mohammad Wazid, Ashok Kumar Das
An “intrusion detection system” (IDS) is useful to monitor and analyze malicious traffic to protect the devices (i.e., smart devices) from the threats. In a cloud computing environment, an IDS verifies all inbound packets and searches for any symptom of intrusion. If a threat is identified, the deployed tools can take proper actions (e.g., notifying the administrators, omitting the source IP address from accessing other resources). In an “IoT-based cloud computing environment,” it is also possible that an adversary may physically capture some of the smart devices. The adversary can also deploy his/her malicious nodes (devices) using the extracted information from the captured devices. In addition, these malicious nodes may be pre-installed with malicious script to launch various attacks (i.e., routing attack) [59,60,63,67]. Upon successful execution of these attacks, the data packets may get lost, dropped, delayed, or modified. It may again cause degradation in performance of communication. This may lead to reduction in “network throughput” and “packet delivery ratio,” and increase in high “end-to-end delay” [63,67]. Therefore, it is essential to design an IDS for protecting communication over the cloud.
Polyaniline Nanostructures
Published in C.W. de Silva, Mechatronic Systems, 2007
The performance gain is illustrated here by comparing the proposed SFA, TPSF+ [15], with Bluenet [13]. In the simulation model, there are 40 nodes in total, and these nodes are placed in an area of 20 × 20 m2. For each data point, the simulation was run 100 times and each run time was 120 seconds. The nonpersistent TCP (Transmission Control Protocol) on/off traffic is used. During the “on” periods, packets are generated at a constant burst data rate of 1440 Kbps. During the “off” periods, no traffic is generated. Burst times and idle times follow the exponential distributions with an average “on” time of 0.5 seconds and an average “off” time of 0.5 seconds. The packet size is 1000 bytes. The performance metrics include the aggregate throughput and the average end-to-end delay. The aggregate throughput is defined as the total throughput obtained by all the communication sessions. The end-to-end delay is determined from the time when the packet is created at the source node to the time when the packet is received at the destination node.
A Systematic Review Paper on Energy-Efficient Routing Protocols in Internet of Things
Published in IETE Journal of Research, 2023
Initially, RPL was designed for LLN networks and works only on lightweight traffic of data networks. As the connection of IoT devices is increasing every day and, in the future, it will connect more devices as shown in Figure 8. From this, we can analyse that data produced by these devices is also going to increase. This is a problem for RPL as it does not work efficiently on the heavily loaded network. This makes the loss of many packets and also increases the end-to-end delay of transmission of data. This can also decrease the growth of IoT networks. When a node sends data to another node, there are chances that buffer overflow occurrence can occur in a congestion environment of IoT. The node sends information to its parent node, in a heavily loaded network there are chances that the node continuously sends data to its over-loaded parent. This creates a parent selection problem and also raises many problems such as packet loss, increases delay, increases energy consumption, and not an inefficient use of a queue (Figure 9).
An enhanced performance through agent-based secure approach for mobile ad hoc networks
Published in International Journal of Electronics, 2018
Dhananjay Bisen, Sanjeev Sharma
As per Figure 14, the effect of network size on the end-to-end delay is shown. End-to-end delay refers to total time consumed through packets to be transmitted across a network from source to destination. In this result, proposed AB-SEP approach is analysed with concept of member balancing and this is performing better because agent nodes estimates number of neighbours using average node degree (AD) that is effectively handled by them. In this condition, degree difference of node () is minimum, by which agent nodes are not overloaded and also results in reduced routing overhead and minimum congestion at agent nodes. In this Figure, when network size increases from 80 to 100 nodes, then delay is increasing because when network size increases, proportionally number of CBR over UDP connections (traffic type) is also increased between nodes. That generates large amount of traffic (transmit packets) between source and destination. So that congestion will occur at nodes and drop the packets, thus nodes retransmit the packets, which generate higher end-to-end delay. Another side, due to malicious nodes, some time transmitted packets are dropped by intermediate nodes so that nodes initiate route discovery process and source node again retransmits the packet that will also generate delay in packet transmission.
A new QoS aware and energy efficient opportunistic routing protocol for wireless sensor networks
Published in International Journal of Parallel, Emergent and Distributed Systems, 2018
Amal Tiab, Louiza Bouallouche-Medjkoune, Samra Boulfekhar
Another important metric in evaluating routing protocols is the average end-to-end delay. This metric represents the time it takes a data packet to reach the destination. This includes all possible delays caused by buffering during route discovery latency, queuing at the interface queue. The lower value of end-to-end delay means the better performance of the protocol.[29] Mathematically, it can be defined as in Equation (12).AEED represents the Average End-to-End Delay.