FollowMe: One Social Importance-Based Collaborative Scheme in MONs

The performance of mobile opportunistic networks mainly relies on collaboration among nodes. Thus far, researchers have ignored the influence of node sociality on the incentive process, leading to poor network performance. Considering the fact that followers always imitate the behavior of superstars, this paper proposes FollowMe, which integrates the social importance of nodes with evolutionary game theory to improve the collaborative behavior of nodes. First, we use the prisoner’s dilemma model to establish the matrix of game gains between nodes. Second, we introduce the signal reference as a game rule between nodes. The number of nodes choosing different strategies in a game round is used to calculate the cumulative income of the node in combination with the probability formula. Finally, the Fermi function is used to determine whether the node updates the strategy. The simulation results show that, compared with the random update rule, the proposed strategy is more capable of promoting cooperative behavior between nodes to improve the delivery rate of data packets.

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A Data Dissemination Method Based on Region Type Correlations for Mobile Opportunistic Networks

Wireless Communications and Mobile Computing ◽

10.1155/2018/1576286 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Linfeng Liu ◽

Daoliang Chen

Keyword(s):

Data Dissemination ◽

Opportunistic Networks ◽

Delivery Ratio ◽

Mobility Patterns ◽

Relay Nodes ◽

Data Packets ◽

Mobile Opportunistic Networks ◽

Communication Devices ◽

Markov Decision ◽

Markov Decision Model

In Mobile Opportunistic Networks (MONs), due to the node movements and the uncontrollable on/off switches of the carried communication devices, the contacts between nodes may be scarce and momentary, and thus a data packet should be transferred through some discrete hops. To avoid the costly flooding of data packets, the data packets are typically disseminated to some relay nodes selected by data holders. However, the mobility patterns of nodes will become different in different types of regions (such as residential regions, commercial regions, scenery regions, or industrial regions); i.e., the movement directions and movement ranges of nodes are frequently varied when the nodes move among various regions. At present, the issues regarding the region types and region type correlations have not been investigated for the data dissemination in existing works. To this end, we propose a Region Type based Data Dissemination Method (RTDDM) for MONs, which exploits the region type correlations and selects the proper relay nodes through a Markov decision model. To verify the performance of RTDDM, we give some theoretical analysis as well as an elaborated simulation study, the results of which show that RTDDM can improve the delivery ratio and reduce the delivery delay, especially in the applications with various region types.

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Spectrum Based Power Management for Congested IoT Networks

Sensors ◽

10.3390/s21082681 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2681

Author(s):

Kedir Mamo Besher ◽

Juan Ivan Nieto-Hipolito ◽

Raymundo Buenrostro-Mariscal ◽

Mohammed Zamshed Ali

Keyword(s):

Power Management ◽

Network Performance ◽

Channel Allocation ◽

Battery Life ◽

Packet Routing ◽

Data Packets ◽

Increasing Demand ◽

Iot Devices ◽

Set Up

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

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Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽

10.3390/electronics10151774 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1774

Author(s):

Ming-Chin Chuang ◽

Chia-Cheng Yen ◽

Chia-Jui Hung

Keyword(s):

Data Center ◽

Completion Time ◽

Network Performance ◽

Data Locality ◽

Task Completion ◽

Data Center Networks ◽

Task Completion Time ◽

Data Packets ◽

Network Bandwidth ◽

Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

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