scholarly journals A Neighbor-Based Probabilistic Broadcast Protocol for Data Dissemination in Mobile IoT Networks

2020 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Data Dissemination ◽  
Recent Trend ◽  
Mobile Nodes ◽  
Node Density ◽  
Sensing Data ◽  
Industrial Companies ◽  
Economical Design ◽  
Probabilistic Broadcast ◽  
Network Overhead ◽  
Broadcast Protocol

The recent trend of implementing Internet of Things (IoT) applications is to transmit sensing data to a powerful data center, and try to discover the valuable knowledge behind ``Big Data'' by various intelligent but resource-consuming algorithms. However, from the discussion with some industrial companies, it is understood that disseminating real-time sensing data to their nearby network-edge-applications directly would produce a more economical design and lower service latency for some important smart city applications. Therefore, this paper proposes an efficient broadcast protocol to disseminate data in mobile IoT networks. The proposed protocol exploits the neighbor knowledge of mobile nodes to determine a rebroadcast delay that prioritizes different packet broadcasts according to their profits. An adaptive connectivity factor is also introduced to make the proposed protocol adaptive to the node density of different network parts. By combining the neighbor knowledge of nodes and adaptive connectivity factor, a reasonable probability is calculated to determine whether a packet should be rebroadcasted to other nodes, or be discarded to prevent redundant packet broadcast. Extensive simulation results have validated that this protocol can improve the success ratio of packet delivery by 13% ~ 28% with a similar end-to-end transmission delay and network overhead of the most state-of-art approaches.

scholarly journals A Neighbor-Based Probabilistic Broadcast Protocol for Data Dissemination in Mobile IoT Networks

2020 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Data Dissemination ◽  
Recent Trend ◽  
Mobile Nodes ◽  
Node Density ◽  
Sensing Data ◽  
Industrial Companies ◽  
Economical Design ◽  
Probabilistic Broadcast ◽  
Network Overhead ◽  
Broadcast Protocol

The recent trend of implementing Internet of Things (IoT) applications is to transmit sensing data to a powerful data center, and try to discover the valuable knowledge behind ``Big Data'' by various intelligent but resource-consuming algorithms. However, from the discussion with some industrial companies, it is understood that disseminating real-time sensing data to their nearby network-edge-applications directly would produce a more economical design and lower service latency for some important smart city applications. Therefore, this paper proposes an efficient broadcast protocol to disseminate data in mobile IoT networks. The proposed protocol exploits the neighbor knowledge of mobile nodes to determine a rebroadcast delay that prioritizes different packet broadcasts according to their profits. An adaptive connectivity factor is also introduced to make the proposed protocol adaptive to the node density of different network parts. By combining the neighbor knowledge of nodes and adaptive connectivity factor, a reasonable probability is calculated to determine whether a packet should be rebroadcasted to other nodes, or be discarded to prevent redundant packet broadcast. Extensive simulation results have validated that this protocol can improve the success ratio of packet delivery by 13% ~ 28% with a similar end-to-end transmission delay and network overhead of the most state-of-art approaches.

scholarly journals Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Chen ◽  
Shengbao Zheng ◽  
Zhengqiu Weng
Keyword(s):  
Mobile Node ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Mobile Crowd Sensing ◽  
Sensitive Data ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called P3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

Bates Distribution Inspired Trust Factor-Based Selfish Node Detection Technique in Mobile Adhoc NETworks

2019 ◽  
Vol 16 (2) ◽  
pp. 609-615
Author(s):  
A. Karthikayen ◽  
Selvakumar S. Raja
Keyword(s):  
Ad Hoc ◽  
Data Dissemination ◽  
Detection Technique ◽  
Mobile Nodes ◽  
Selfish Behavior ◽  
Data Packets ◽  
Selfish Node ◽  
Huge Data ◽  
Multiple Levels ◽  
Node Isolation

The trustworthiness of the mobile nodes is considered as the predominant parameter for ensuring significant data dissemination in the ad hoc network. However, the selfishness activity of the mobile nodes minimizes the trust of the mobile nodes by dropping a considerable number of data packets in the network. The significant dropping of data packets by the selfish node introduces huge data overhead with increased latency and energy consumptions by increasing the number of retransmissions. In this paper, a Bates Distribution Inspired Trust Factor-based Selfish Node Detection Technique (BDITF-SNDT) is proposed for predominant detection of selfish behavior by investigating multiple levels of factors that contribute towards effective selfishness detection. This proposed BDITF-SNDT approach is also potent in enhancing the detection rate of selfishness through the multi-perspective analysis of each monitored mobile nodes' forwarding characteristics towards the benefits of the other interacting mobile nodes. The simulation experiments and results of the proposed BDITF-SNDT approach is determined to be enhanced on an average by 16% and 14% superior to the compared selfish node isolation approaches existing in the literature.

scholarly journals My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination

2019 ◽  
Vol 11 (2) ◽  
pp. 29 ◽  
Author(s):  
Asanga Udugama ◽  
Jens Dede ◽  
Anna Förster ◽  
Vishnupriya Kuppusamy ◽  
Koojana Kuladinithi ◽  
...  
Keyword(s):  
Data Dissemination ◽  
A Priori ◽  
Opportunistic Networks ◽  
Local Context ◽  
A Priori Knowledge ◽  
Data Forwarding ◽  
Extensive Simulation ◽  
Q Learning ◽  
Final Destination ◽  
Network Overhead

Opportunistic networks have recently seen increasing interest in the networking community. They can serve a range of application scenarios, most of them being destination-less, i.e., without a-priori knowledge of who is the final destination of a message. In this paper, we explore the usage of data popularity for improving the efficiency of data forwarding in opportunistic networks. Whether a message will become popular or not is not known before disseminating it to users. Thus, popularity needs to be estimated in a distributed manner considering a local context. We propose Keetchi, a data forwarding protocol based on Q-Learning to give more preference to popular data rather than less popular data. Our extensive simulation comparison between Keetchi and the well known Epidemic protocol shows that the network overhead of data forwarding can be significantly reduced while keeping the delivery rate the same.

scholarly journals Trust and Energy-Inspired Threshold Packet Relaying Capability Technique (TEITPRCT) for Reliable Routing in MANETs

2019 ◽  
Vol 8 (12) ◽  
pp. 3612-3617
Keyword(s):  
Data Dissemination ◽  
Mobile Node ◽  
Reliable Data ◽  
The Other ◽  
Mobile Nodes ◽  
Data Forwarding ◽  
Simulation Experiments ◽  
Frequency Index ◽  
Energy Consumptions ◽  
Simulation Results

The data dissemination in MANET completely depends on the packet relaying capability attributed by the mobile nodes of the network. This packet relaying potential of the mobile node completely depends on the energy possessed by the mobile nodes. In this paper, a Trust and Energy-Inspired Threshold Packet Relaying Capability Technique (TEITPRCT) is proposed for achieving reliable data forwarding in the network. This proposed TEITPRCT approach utilized the benefits of factors that includes Frequency Index of Interaction (FII), Intimacy Index (II) and Honesty Index (HI) for quantifying the trust attributed by each mobile node in the network. It also inherently estimates the energy possessed by the mobile nodes of the network through the utilization of probe packets. The simulation experiments are conducted using ns-2 based on evaluation metrics that include throughput, latency, and energy consumptions with respect to different mobile nodes of the network. The simulation results proved the potentiality of the proposed TEITPRCT approach over the other benchmarked schemes considered for investigation

Sign in / Sign up

Export Citation Format

Share Document