A framework for WSN-based opportunistic networks

One of the purposes of Internet of Things (IoT) is to reach more deeper perception. For this purpose, the efficient energy consumption is necessary among intelligent devices that make up the part of Opportunistic Networks (ONs). It is irrational for an ONs without any sleep scheduling because of awful user experience. We explore a sleeping schedule which is based on duty cycling for mobile devices to reduce energy consumption of ONs. To see how schedule affects the performance of ONs, we took a series of simulations and the results indicated that the sleeping schedule is an efficient method for prolonging the network life time in ONs. The successful delivery ratio can increase two to three times when factor Trequal 0.2. We also observed that the network matrices are acceptable, and the network survival time can be extended effectively in ONs.

In opportunistic networks the communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The enormous growth of nodes having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and nodes, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT nodes in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-based System for IoT node selection in opportunistic networks. For our proposed system, we use four input parameters: Node’s Free Buffer Space (NFBS), Node’s Successful Delivery Ratio (NSDR), Node’s Battery Level (NBL), Node Contact Duration (NCD). The output parameter is Node Selection Decision (NSD). The results show that the proposed system makes a proper selection decision of IoT nodes in opportunistic networks. The IoT node selection is increased up to 17% and 40% by increasing NBL and NSDR, respectively.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce η-guaranteed delivery which means that the ratio of successful data deliveries is not less than η, and study the relationship between the transmission power of sensors and the probability of achieving η-guaranteed delivery. Furthermore, with considering the effect of network congestion and link collision, we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving η-guaranteed delivery.

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.

Summary form only given. Service advertisement and discovery are important components for collaborative applications in mobile ad hoc networking (MANET) environment since they enable mobile communication and computing entities to provide services and associated data to peers and to be aware of and use the available services and data from peers. In this research, we design and implement a lightweight protocol for service advertisement and discovery based on a MANET multicast protocol ODMRP (on-demand multicast routing protocol). Service advertisement and discovery information is piggybacked in ODMRP routing control packets. NS-2 simulation results show that our protocol achieves the goal of service advertisement and discovery in mobile ad hoc networks with high successful data delivery ratio and low overheads.

An agent based geographic routing protocol is proposed to improve the well-known geographic routing protocol-GPSR routing protocol. In the proposed scheme, the agent is selected by sink node which concern about the source node`s position as well as agent candidate`s state. So packets will first be forwarded to agent and next step is to be forwarded to their final goal- sink node from agent. During the next hop selection process, nodes select their neighbors by considering not only position but also their average available buffer size. This results in efficient selection of next hop node in congestion area, and then increases the successful packet delivery ratio. The simulation is conducted for two scenarios: general number of connections and large number of connections in our map. Results show that new method with agent achieves improved performance in successful packet delivery ratio when compares to GPSR without our scheme.

Disjoint multipath routing is an attractive approach for improving the reliability of data delivery in wireless sensor networks. Recently, the issue for maintaining the robustness and efficiency of the disjoint multipath routing against network failures such as network holes are being studied. Several protocols have been proposed to handle the network failures. However, they cause the large performance degradation on data delivery ratio and delay due to its centralized manner for detouring the network holes in irregular network environments. Thus, we propose a Localized Disjoint Multipath Routing protocol called LDMR which can achieve reliable data delivery ratio and stable data delivery delay while handling network failures. LDMR constructs the fully disjoint multipath and manages them by using a localized management method with a path priority rule. Simulation results show that our protocol provides the more stable and reliable performance than the previous protocols in terms of packet delivery ratio and packet delivery delay.

Machine learning-driven routing optimization for energy-efficient 6G-enabled wireless sensor networks

QoS-oriented packet scheduling scheme for opportunistic networks

Assessments on routing protocols for delay tolerant network (DTN) or opportunistic network considered various metrics such as delivery ratio, overhead and delay, but energy consumption as a metric is not well analyzed. Since the energy aspect is now an issue, routing protocols should be considered from an energy consumption point of view. In this article, some basic routing protocols of the opportunistic network analyzed from the energy aspect alongside other standard metrics. Afterward, the performance of each routing protocol for different situations assessed through network effective factors. Finally, the simulation results show that Spray and Wait algorithm has better energy performance compared to Epidemic and PRoPHET. Analyzing a protocol with a single-relay selection that minimizes the transmission energy and increase the energy efficiency of all devices in a wireless network. This technique is analyze the opportunistic relaying in the paradigm of D2D communication and study the opportunistic schemes with a single relay selection to minimize energy consumption of the devices in a wireless network. The fact is that The D2D communication ensures that the energy consumed for the data relaying is negligible and The D2D communication ensures that the selection of the relaying device exploits the diversity from neighboring devices in order to minimize the energy consumed by the devices for forwarding the data to the BS.

The vehicular ad hoc network (VANET) is a wireless technology adopted by the research community to implement smart transportation applications. Due to the variation in the vehicular speed, topology in the VANET continuously changes that make a routing complex task. It is better to use the position-based routing protocol that is adaptive and mostly suitable for dynamically changing road environments. The objective of the paper is to analyze an impact of real mobility traces on position-based routing protocols using simulation. It is difficult to perform a simulation in the VANET due to the restricted movement of vehicles. Vehicular mobility plays a major role in the VANET. Realistic mobility traces such as intelligent driver model-intersection management (IDM-IM), IDM–lane changing (LC) and ManhattanGrid are generated using VANETMobiSim and Bonnmotion tool. These traces are applied to the intersection-based routing (IBR), greedy perimeter stateless routing (GPSR) and ant colony optimization-intersection-based routing (ACO-IBR). The impact of the mobility traces has been evaluated with parameters such as delay, throughput and successful delivery ratio (SDR). The result shows that the selection of proper mobility pattern is a key to achieve the realistic performance of the simulation. ACO-IBR minimizes delay and produces better successful delivery ratio and throughput for all real mobility patterns.

In cognitive radio networks (CRNs), the spectrum utilization can be dramatically improved with the secondary users (SUs) accessing the unoccupied licensed channels opportunistically. However, how to fully utilize the spectrum holes to meet the quality of service requirements of SUs is still an open issue. In this paper, we focus on multi-user, multi-channel case, and analyze the stable channel state after allocating packets over a licensed channel. We first assume that the SUs' packet arrival rate obeys Poisson distribution, and the lost packets will be retransmitted with exponential backoff delay. Then, we analyze the stable channel state when a SU selects and allocates certain percentage of its packets over one channel. The theoretic analysis shows that such stable channel state can be solved by the steady-state equations. Based on such stable channel state analysis, we propose a novel greedy packet allocation scheme in multi-user and multi-channel S-ALOHA system. The proposed packet allocation scheme will obtain a maximal spectrum utilization, which will support more SUs to share the spectrum holes or cause less access conflict with PUs. In the simulation part, we assume the PUs' channel access pattern follows Markov model, and study the spectrum efficiency after packet loading over licensed channels and make comparisons with different packet allocation schemes. The simulation results show that the proposed packet allocation scheme outperforms other related works in terms of successful packet delivery ratio, the number of collision packets, and spectrum efficiency.

Mobile crowd-sensing network is a kind of networks that take advantage of pervasive mobile devices to efficiently collect data and enable the completion of large-scale and complicated sensing tasks through the wide participation of mobile users. Data are delivered based on the opportunistic contacts among mobile uses, and the selection of relay nodes plays a vital role in the performance of such network. In most of existing routing mechanisms, data exchange is still limited between two nodes and they always pursue the maximization of single performance objective, which results in extreme imbalance of performance. Unfortunately, this issue has not yet aroused the full attention of researchers by now. In this paper, to deal with this issue, we estimate the network performance metrics such as delivery success ratio and delivery delay according to the movement model. Aiming at the multi-node meeting scene that often appears in real life, the action of selecting relay nodes is modeled as a multi-player cooperative game with the game goal of maximal performance balancing, and an MCGR-PB was proposed, which can redistribute the packets among the contacting nodes based on Nash equilibrium solution to achieve higher performance balancing. Extensive simulations based on a real-life mobility trace and a synthetic trace have been done, and the results show that compared with other protocols, MCGR-PB can greatly balance the overall performance while ensuring the desired delivery performance of the network.

Many real-time routing mechanisms have been proposed to support the newly developed wireless sensor networks (WSNs) applications such as the transmission and retrieval of multimedia traffic. However, the inherent source constraints of sensor network and instability of wireless communication set quite a problem for the existing routing mechanisms to meet the quality of service (QoS) requirements of some specific QoS-aware applications. Hence, real-time fault-tolerant schemes are highly desired for WSNs to address these challenges. In this paper, we propose an -firm-based real-time fault-tolerant mechanism, which helps routing mechanisms to achieve specific QoS requirement by employing a local status indicator (LSI) at each sensor node to monitor and evaluate the local conditions of node and network. Therefore, specific fault recovery mechanisms could be implemented for ensuring an acceptable QoS performance, according to the evaluated LSI values. By using this fault-tolerant scheme, each node dynamically adjusts its transmission capability to mitigate the performance degradation of real-time service caused by network faults and to maintain the desired reliability and timeliness. Simulation result shows that LSI cannot only help to reduce the effects of congestion, link failure, and void, but also reach higher successful transmission ratio and smaller transmission delay.

Geographic routing has been considered as an attractive approach for resource-constrained wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, this routing approach often suffers from the routing hole (i.e., an area free of nodes in the direction closer to destination) in various environments such as buildings and obstacles during data delivery, resulting in route failure. Currently, existing geographic routing protocols tend to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, these protocols cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we focus on addressing these issues and propose an energy-aware dual-path geographic routing (EDGR) protocol for better route recovery from routing holes. EDGR adaptively utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into threedimensional (3D) sensor networks to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared to other geographic routing protocols in WSNs over a variety of communication scenarios passing through routing holes. The proposed EDGR is much applicable to resource-constrained WSNs with routing holes.