Opportunistic Sensor Networks Research Articles

Identifying key node in multi-region opportunistic sensor network based on improved TOPSIS

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network.

Incorporating security in opportunistic routing and traffic management in opportunistic sensor network

Nowadays, wireless sensors or opportunistic sensor networks (OSN) and its technologies are mainly used for bridging the spaces between the physical world and the virtual electronics. Due to several potentials like limited storage, unreliable communication, higher latency in communication and unattended operations of networks and sensor node powers, OSN faces many problems on routing and traffic management. Security on OSN involves routing and data aggregation deployed in OSN involves collaborations among nodes on the network due to ad-hoc nature. To solve these problems, our proposed method involves the novel traffic management scheme and opportunistic secure routing on sensor networks. This method involves mainly three algorithms such as range-based clustering (RBC) for clustering the sensor nodes, minimum waiting time routing (MWTR) algorithm for routing the data packets and in order to provide secure communication while coalition attack and replica attacks present on the network, we propose light weight key generation mechanism (LWKG). Thus, our proposed system provides less computational complexity on the networks and we provide effective results on experimental evaluation based on average packet reception ratio, detection ratio of replicas, connectivity and coalition attack resistance.

Incorporating security in opportunistic routing and traffic management in opportunistic sensor network

Nowadays, wireless sensors or opportunistic sensor networks (OSN) and its technologies are mainly used for bridging the spaces between the physical world and the virtual electronics. Due to several...

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

A Multi-Technology Communication Platform for Urban Mobile Sensing.

A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network.

A link prediction approach based on deep learning for opportunistic sensor network

Link prediction for opportunistic sensor network has been attracting more and more attention. However, the inherent dynamic nature of opportunistic sensor network makes it a challenging issue to ensure quality of service in opportunistic sensor network. In this article, a novel deep learning framework is proposed to predict links for opportunistic sensor network. The framework stacks the conditional restricted Boltzmann machine which models time series by appending connections from the past time steps. A similarity index based on time parameters is proposed to describe similarities between nodes. Through tuning learning rate layer-adaptively, reconstruction error of restricted Boltzmann machine goes stable rapidly so that the convergence time is shortened. The framework is verified by real data from INFOCOM set and MIT set. The results show that the framework can predict links of opportunistic sensor network effectively.

Connected model for opportunistic sensor network based on Katz centrality

Connectivity is an important indicator of network performance. But the opportunistic sensor networks (OSNs) have temporal evolution characteristics, which are hard to modelled with traditional graphs. After analyzing the characteristics of OSNs, this paper constructs OSNs connectivity model based on time graph theory. The overall connectivity degree of the network is defined, and is used to estimate actual network connectivity. We also propose a computing method that uses the adjacency matrix of each snapshot. The simulation results show that network connectivity degree can reflect the overall connectivity of OSNs, which provide a basis for improving the OSNs performance.

Incorporating privacy and security in military application based on opportunistic sensor network

Wireless ad-hoc and sensor networks (WASN) or opportunistic sensor networks (Oppnets) have been improving their availability in providing secure communication between military candidates in military applications. The existing work has not addressed this issue effectively. This paper proposes secure route selection and three different encryption algorithms for Commando-Commando (CC), Soldier-Soldier (SS) and Commando-Soldier (CS) communication. Routing is based on path protected hop-by-hop routing protocol (PPHH) by which a secure path is selected with authentication by certificate authority (CA). The cryptography algorithms used are code-based cryptography, hyperelliptic curve cryptography, and design-based cryptography. The proposed strategy in this paper shows improvements in security, energy consumption, throughput, packet overhead and network reliability.

Incorporating privacy and security in military application based on opportunistic sensor network

Incorporating privacy and security in military application based on opportunistic sensor network

Optimal and quasi‐optimal energy‐efficient storage sharing for opportunistic sensor networks

AbstractThis paper investigates optimum distributed storage techniques for data preservation, and eventual dissemination, in opportunistic heterogeneous wireless sensor networks where data collection is intermittent and exhibits spatio‐temporal randomness. The proposed techniques involve optimally sharing the sensor nodes' storage and properly handling the storage traffic such that the buffering capacity of the network approaches its total storage capacity with minimum energy. The paper develops an integer linear programming (ILP) model, analyses the emergence of storage traffic in the network, provides performance bounds, assesses performance sensitivities and develops quasi‐optimal decentralized heuristics that can reasonably handle the problem in a practical implementation. These include the Closest Availability (CA) and Storage Gradient (SG) heuristics whose performance is shown to be within only 10% and 6% of the dynamic optimum allocation, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM). It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.

Performance Evaluation of Joint Routing and Scheduling in Opportunistic Sensor Networks

In present scenario the innovation of mobile phones that are embedded with different types of onboard sensors, has brought on a new interest of using them as the main part of the sensor network, for a wide area of applications (for e.g. monitoring). In such ad-hoc network end to end connectivity cannot be established and the connection comes out to be mainly opportunistic. The connection in Opportunistic Sensor Network (OSN) is many to one i.e. all the nodes have a common destination. The prominent challenge faced in the opportunistic sensor networks includes the Routing /Scheduling and managing the constrained memory. In this paper scheduling is defined by routing combined with proper buffer management policy and since the scheduling in OSN is node based so selection of proper Buffer management is also significant. This paper considers the monitoring application with the help of human carried mobile sensors and hence a performance evaluation of joint Routing/Scheduling and Buffer management using simulator called ONE (Opportunistic Network Environment) Simulator is done. Simulation results indicate superiority of Shortest remaining Life Time (SHLI) scheme for node movement as per human mobility characteristics. This is contrary to community mobility model as shown for PROPHET algorithm, in which Most Forwarded First (MOFO) scheme performs better.

A connectivity monitoring model of opportunistic sensor network based on evolving graph

Connectivity is one of the most important parameters in network monitoring. The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be established by traditional graph models due to the fact that its connectivity is timing correlative and evolutionary, which makes it extremely difficult to monitor an OSN. In order to solve the monitoring problem, this paper builds an evolving graph model based on the theory of evolving graph as a description of an OSN. It defines a series of parameters to measure the connectivity of the OSN and establishes an monitoring model. Meanwhile, this paper gives the key algorithms in building the model, the Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ) algorithm. The rationality of the monitoring model has been proven by a prototype system and the simulation results. Extensive simulation results show that the proposed connectivity monitoring model can indicate real circumstances of OSN? connectivity, and it is applicable to monitoring an opportunistic sensor network.

A connectivity monitoring model of opportunistic sensor network based on evolving graph

Connectivity is one of the most important parameters in network monitoring. The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be established by traditional graph models due to the fact that its connectivity is timing correlative and evolutionary, which makes it extremely difficult to monitor an OSN. In order to solve the monitoring problem, this paper builds an evolving graph model based on the theory of evolving graph as a description of an OSN. It defines a series of parameters to measure the connectivity of the OSN and establishes an monitoring model. Meanwhile, this paper gives the key algorithms in building the model, the Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ) algorithm. The rationality of the monitoring model has been proven by a prototype system and the simulation results. Extensive simulation results show that the proposed connectivity monitoring model can indicate real circumstances of OSN’ connectivity, and it is applicable to monitoring an opportunistic sensor network.

Agent Based Predictive Data Collection in Opportunistic Wireless Sensor Network

Abstract Opportunistic wireless sensor networks have significant applications like remote monitoring where node density is usually low than average. The traditional deployment of sensor networks has larger density, confirmed communication links and multiple routing paths. This does not apply to opportunistic networks. Due to sensing nodes constraints and sparse connectivity, the data collection in OWSN is a difficult task. Mobile agents can be injected into the network for performing network exploration to collect metadata of nodes. Ant-AODV routing protocol provides least overhead in mobile agent routing. Based on the metadata a predictive model can be built to find the probable contact time and location of the mobile nodes and sink in OWSN. This paper simulates an agent based predictive data collection algorithm (PLA). The simulation results are then compared with other opportunistic algorithms and the data collection performance improves almost by 33.2%.

Adaptive Collaborative Detection for Opportunistic Vehicle Sensor Networks

Transportation is a huge problem that curbs development of societies and economy in many countries nowadays. Participatory sensing technologies encourage people to be involved in environment monitoring through smart devices. Vehicle sensor networks (VSNs) are a novel solution for road event detection with advantages. In this paper, we consider runtime road event detection using VSNs to satisfy the application-specific requirements through collaboration among vehicles. A group road detection scheme (GRD) by using dynamic clustering in VSNs is presented to improve detection performance with low time complexity and message complexity. The simulations with testbed of 5 remote controllable vehicles show that GRD scheme provides effectiveness under different road scenarios.

Data Gathering in Opportunistic Wireless Sensor Networks

The wireless sensor networks and opportunistic networks have nowadays presented a trend of technology convergence. On one hand, the nodes periodically sense the environment and continuously generate sensing data; on the other hand, the movements and sparse deployment of nodes usually lead to intermitted connected links and create some form of opportunistic communications. So it is a challenging problem to effectively collect the sensing data in opportunistic wireless sensor networks. In this paper, we propose an efficient data gathering algorithm based on location prediction in opportunistic wireless sensor networks. The algorithm first collects the network metadata such as history of node encounters and contact durations; then it creates a node contact graph, based on which predictive optimal data gathering locations are dynamically calculated and updated. Finally, the sink is controlled to move to these locations to collect sensing data, avoiding lots of unnecessary data exchanges and message transmissions. Extensive experimental results show that the proposed algorithm is effective to reduce the message transmissions and improve the data collection coverage rate.

An Autonomous Packet Transmission Strategy for Opportunistic Wireless Sensor Networks

We consider a wireless sensor network that comprises a single hop between the sensor nodes and the central controller node with multiple antennas. In this system model, we concentrate on the single-beam opportunistic communication and propose two novel packet transmission schemes that can perform multiuser diversity gain based on the signal-to-noise ratio (SNR) and the normalized SNR (NSNR) measurements at the sensor nodes with single antenna. The main objective of the multiuser diversity is to increase the total throughput over the fast fading channels. Proposed schemes are based on the principle of cross-layer design that integrates with physical layer characteristics of wireless channel and medium access control (MAC) layer characteristics of network. In our design, we assume that the sensor nodes know only their own channels to the controller node and the packet transfer from the sensor nodes to the controller node is initiated when the channel quality of any node exceeds the predefined threshold at the current time slot. To determine the optimum threshold, we maximize the probability of successful packet transmission where only one sensor node transmits its packet in one time slot under the simplified collision model. Simulation results are demonstrated to compare the performance of the proposed schemes in terms of throughput, energy efficiency, and fairness. The obtained results show that the presented opportunistic strategy can be used to improve the network throughput.

Data Dissemination Scheme Research in Opportunistic Sensor Networks

This paper mainly discusses the performance of three typical data dissemination schemes in opportunistic sensor networks, which include rumor spread protocol, direct dissemination protocol and the protocol based on evolvement strategy. The main idea of the protocol based on evolvement strategy is that in the process of data dissemination, those nodes with higher dissemination validity are dynamically to substitute the node with lower dissemination validity. This paper makes detail theory analysis on the performance of these protocols. The performance of these protocols also is compared with simulation experiments further more.