Opportunistic Internet Of Things Research Articles

SMART: Secured and Mobility Aware Routing Technique for Opportunistic IoT Network in Smart Cities

Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.

A Hybrid Opportunistic IoT Secure Routing Strategy Based on Node Intimacy and Trust Value

Routing strategy is one of the most important researches in Opportunistic Internet of Things (IoT), and it highly influences the efficiency of data transmission. In this paper, a hybrid Opportunistic IoT secure routing strategy based on node intimacy and trust value (HIRouter) is proposed to resolve the problem of unbalanced transmission efficiency and security in the message delivery process. According to the records of node encounter and message forwarding, the strategy proposed in this paper can calculate nodes’ intimacy and trust value. The messages are then forwarded based on the intimacy and trust value between nodes. Experimental results verify that HIRouter algorithm we proposed can improve the message delivery rates and reduce the overhead rate in the Opportunistic IoT with dense nodes and frequent interactions between nodes.

Mobile Collectors for Opportunistic Internet of Things in Smart City Environment with Wireless Power Transfer

In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs has been well studied and presents several advantages compared to the traditional methods that employ static sinks. However, data collection and transmission from the hundreds of thousands of sensors sparsely distributed across virtually every smart city has raised some new challenges. One of these concerns lies in how these sensors are being powered as majority of the IoT sensors are extremely energy-constrained owing to their smallness and mode of deployments. It is also evident that sensor-nodes closer to the sinks dissipate their energy faster than their counterparts. Moreover, battery recharging or replacement is impractical and incurs very large operational costs. Recent breakthrough in wireless power transfer (WPT) technologies allows the transfer of energy to the energy-hungry IoT sensor-nodes wirelessly. WPT finds applications in medical implants, electric vehicles, wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), mobile phones, and so on. The present study highlights the use of mobile collectors (data mules) as wireless power transmitters for opportunistic IoT-SC operations. Specifically, mobile vehicles used for data collection are further exploited as wireless power transmitters (wireless battery chargers) to wirelessly recharge the energy-constrained IoT nodes placed within their coverage vicinity. This paper first gives a comprehensive survey of the different aspects of wireless energy transmission technologies—architecture, energy sources, IoT energy harvesting modes, WPT techniques and applications that can be exploited for SC scenarios. A comparative analysis of the WPT technologies is also highlighted to determine the most energy-efficient technique for IoT scenarios. We then propose a WPT scheme that exploits vehicular networks for opportunistic IoT-SC operations. Experiments are conducted using simulations to evaluate the performance of the proposed model and to investigate WPT efficiency of a power-hungry opportunistic IoT network for different trade-off factors.

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.

Grant-Free Opportunistic Uplink Transmission in Wireless-Powered IoT: A Spatio-Temporal Model

Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for wireless-power Internet of Things (IoT) networks. This article jointly characterizes energy harvesting and packet transmissions in grant-free opportunistic uplink IoT networks energized via harvesting downlink energy. To do that, a joint queuing theory and stochastic geometry model is utilized to develop a spatio-temporal analytical model. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC). Such two dimensions are utilized to jointly track the scavenged/depleted energy to/from the batteries along with the arrival/departure of packets to/from devices buffers over time. Consequently, the adopted queuing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average delay, and the average buffer size. The effect of base stations (BSs) densification is discussed and several design insights are provided. The results show that the parameters for uplink power control and opportunistic channel access should be jointly optimized to maximize average network packet throughput, and hence, minimize delay.

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

An improved PRoPHET - Random forest based optimized multi-copy routing for opportunistic IoT networks

Opportunistic networks are one of the important categories of ad hoc networks in Internet of Things (IoT), which considers human social activities like daily routines, activities and many more to provide efficient communication. In opportunistic networks, mobile nodes are used to establish communication between nodes despite of non-availability of a dedicated route between them. Furthermore, nodes don’t acquire any knowledge in advance about the characteristics of the network such as the network topology and the location of the other nodes. Hence, designing a routing algorithm becomes a challenging task since traditional routing protocols used in the Internet are not feasible for the characteristics inherent type of network. The proposed work propounds a multi-copy routing algorithm based on machine learning named iPRoPHET or improved PRoPHET (Probability routing protocol using history of encounters and transitivity). iPRoPHET, uses dynamically changing contextual information of nodes and the delivery probability of PRoPHET to carry out message transfer. The iPRoPHET uses machine learning classifier known as random forest to classify the node as a reliable forwarder or a non-reliable forwarder based on the supplied contextual information during each routing decision. The classifier trained with large amount of data extracted using simulation leads to precise classification of the nodes as reliable or unreliable nodes for carrying out the routing task. Obtained results from the simulation proves that the proposed algorithm outperforms with respect to delivery probability, hop count, overhead ratio, latency but over costs with respect to average buffer time in par with similar multi-copy routing algorithms. The uniqueness of this paper lies in data extraction, categorization and training the model to obtain reliable and unreliable nodes to facilitate efficient multi-copy routing in IoT communication.

T_CAFE: A Trust based Security approach for Opportunistic IoT

Internet of things (IoT) is a revolution of the internet where a group of computing devices, sensors, machines or people, having unique identifiers and the ability to transfer data over the network without human intervention, are interconnected. Opportunistic networks (OppNets) are a type of disruption-tolerant networks, where network topology is not fixed and the devices are connected intermittently. Opportunistic IOT (OppIoT) is a blend of OppNets and IoT networks, where the data are shared among IoT devices and human communities exploiting the opportunistic contact nature of humans. The data is usually transmitted in a broadcast manner, exposing it to all the members of the network. Thus, securing the data transmitted is of utmost importance in OppIoT. This article proposes a trust-based schemE (called T_CAFE ) for securing the network against several attacks like sybil, bad mouthing, good mouthing, black hole and packet fabrication attacks. Using the opportunistic network environment simulator for performing simulations, it is found that the proposed T_CAFE protocol enhances the network security and outperforms routing protocols such as SHBPR, RSASec and ATDTN in terms of legitimate packet delivery, higher probability of message delivery, lower count of dropped messages and lower value of latency in packet delivery.

Modelling and simulation of Opportunistic IoT Services with Aggregate Computing

The Internet of Things (IoT) is emerging as a ubiquitous and dense ecosystem in which novel devices and smart objects interoperate to establish smart cities, smart buildings, etc. In such application contexts, a plethora of innovative services are expected to stand out, deeply impacting our daily routine. In particular, real IoT drivers will be cyberphysical, collective, highly dynamic and contextualised services, called in the following Opportunistic IoT Services. This work proposes a full-fledged approach for their development, based on (i) a technology-agnostic yet detailed modelling phase, which allows opportunistic properties to emerge since the preliminary service analysis; and (ii) the implementation and further simulation of IoT services through Aggregate Computing, a distributed computing paradigm and engineering stack able to harness, in practice, the dynamic, collective and context-driven nature of Opportunistic IoT Services. A mass event case study, related to the real-world scenario of a large scale urban crowds detection and steering, provides evidence of the huge potential of the approach: indeed, simulation results highlight the effectiveness, flexibility, scalability and resilience of the Aggregate Computing-based approach to the design of Opportunistic IoT Services.

Impact of Node Speed on Energy-Constrained Opportunistic Internet-of-Things with Wireless Power Transfer.

Wireless power transfer (WPT) is a promising technology to realize the vision of Internet-of-Things (IoT) by powering energy-hungry IoT nodes by electromagnetic waves, overcoming the difficulty in battery recharging for massive numbers of nodes. Specifically, wireless charging stations (WCS) are deployed to transfer energy wirelessly to IoT nodes in the charging coverage. However, the coverage is restricted due to the limited hardware capability and safety issue, making mobile nodes have different battery charging patterns depending on their moving speeds. For example, slow moving nodes outside the coverage resort to waiting for energy charging from WCSs for a long time while those inside the coverage consistently recharge their batteries. On the other hand, fast moving nodes are able to receive energy within a relatively short waiting time. This paper investigates the above impact of node speed on energy provision and the resultant throughput of energy-constrained opportunistic IoT networks when data exchange between nodes are constrained by their intermittent connections as well as the levels of remaining energy. To this end, we design a two-dimensional Markov chain of which the state dimensions represent remaining energy and distance to the nearest WCS normalized by node speed, respectively. Solving this enables providing the following three insights. First, faster node speed makes the inter-meeting time between a node and a WCS shorter, leading to more frequent energy supply and higher throughput. Second, the above effect of node speed becomes marginal as the battery capacity increases. Finally, as nodes are more densely deployed, the throughput becomes scaling with the density ratio between mobiles and WCSs but independent of node speed, meaning that the throughput improvement from node speed disappears in dense networks. The results provide useful guidelines for IoT network provisioning and planning to achieve the maximum throughput performance given mobile environments.

A location Prediction-based routing scheme for opportunistic networks in an IoT scenario

Opportunistic Internet of Things (OppIoT) is a paradigm, technology, and system that promotes the opportunistic exploitation of interactions between IoT devices to achieve increased connectivity, reliability, network capacity, and overall network lifetime. The increased demand for identifying such opportunistic exploitation is illustrated by IoT scenarios, where the goal is to recognize when an opportunity for communication is possible, thereby allowing for data forwarding and routing. In an OppIoT system, devising a routing scheme is a challenging task due to the difficulty in guaranteeing the existence of connectivity between devices (nodes) and in identifying an intermediate node as a packet forwarder towards its destination. Considering that opportunistic networks (oppNets) are a subclass of OppIoT and considering IoT scenarios where the opportunistic exploitation of IoT devices is possible even in case the device’s presence is uncertain or may change over time, this paper proposes a novel routing scheme for OppNets (called Location Prediction-based Forwarding for Routing using Markov Chain (LPFR-MC)) that can also be used in IoT scenarios. The proposed LPFR-MC scheme considers the node’s present location and the angle formed by it and the corresponding source (resp. destination) to predict the node’s next location or region using a Markov chain and to determine the probability of a node moving towards the destination. Simulation results are provided, showing that the proposed LPFR-MC outperforms the existing traditional protocols in terms of message delivery probability, hop count, number of messages dropped, message overhead ratio, and average buffer time.

A game theoretic context-based routing protocol for opportunistic networks in an IoT scenario

In an Opportunistic Internet of Things (here denoted oppIoT), a novel paradigm and system that is expected to be an integral part of 5G wireless networks, information dissemination and sharing among opportunistic communities formed by opportunistic contacts using short- range communication and sensing technologies, as well as human mobility, will prevail. In such systems, designing a routing protocol is a major challenge due to the inherent difficulty in dealing with the design constraints associated with such protocols such as node mobility, characterization of the users social behavior, and characterization of the contacts between the opportunistic communities, to name a few. Nonetheless, from a data forwarding perspective, opportunistic networks (oppNets) are a subclass of oppIoT. As such, any novel routing protocol for infrastructure-less oppNet can also find application in an OppIoT system. In this regard, this paper proposes a novel optimized routing protocol (called Game Theoretic Approach for Context Based Routing (GT-ACR)) for oppNets that uses game theory for selecting the best possible next-hop to forward data packets efficiently. In this protocol, the best strategy for the selection of the next hop node is dependent upon a non-zero sum cooperative game of two players considering the context information, encounter index, and distance of the corresponding node from the destination as vital attributes in framing the game. Simulation results are provided, showing that out of the existing four protocols namely Epidemic, Prophet, ProWait and HBPR, the ProWait protocol performed better than the remaining three protocols. However the proposed GT-ACR outperforms even the ProWait in terms of average latency by 15.74%, hop count by 5.3%, number of messages dropped by 14.2% and overhead ratio by 13.6% respectively when number of nodes are varied.

An Arrival and Departure Time Predictor for Scheduling Communication in Opportunistic IoT.

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.

Enabling IoT interoperability through opportunistic smartphone-based mobile gateways

In the near future, all our everyday things and objects will be both connected to the Internet and equipped with enough sensing, acting and processing capabilities to exploit the full potential benefits of the so called Internet of Things (IoT) paradigm. Even the simplest objects will become “smart” because they will be interconnected to other objects to share and collect data from the environments in which they are placed thus paving the way to novel application services, computing and communication scenarios.In this context, interoperability among different standards and communication technologies is still a significant challenge that we have started to address by proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet. The presented unified, high-level and extendible software architecture supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.A specific testbed on common smartphones with different hardware and software capabilities was deployed to evaluate the real feasibility of the designed solution measuring the system performance in terms of energy consumption, memory and CPU usage in high and low load scenarios. According to the obtained results, the implemented software architecture for multi-standard and multi-technology interoperation presents a reduced use of hardware resources in front of a relatively high energy consumption value, mostly due to the simultaneously active radio interfaces combined with a small battery capacity, that limits the smartphone lifetime. Nevertheless, the presented general approach is still remarkable because this latter aspect will most likely be exceeded, in a short time, thanks to daily technological advancements in both batteries and radio interfaces.

Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things

The traditional view of Internet of Things (IoT) attempts to connect all the physical objects to build a global, infrastructure-based IoT. In this paper, however, we will present opportunistic IoT, which is formed based on the ad hoc, opportunistic networking of devices (e.g., mobile phones and smart vehicles) using short-range radio techniques (e.g., Bluetooth and Wi-Fi). The opportunistic IoT demonstrates inherently the close relationship between human and opportunistic connection of smart things. It enables information forwarding and dissemination within and among the opportunistic communities formed based on the movement and opportunistic contact nature of human. We characterize the bi-directional effects between human and opportunistic IoT, discuss the technical challenges faced by this new research field, and propose a reference architecture for developing opportunistic IoT systems. Some of our ongoing practices, including opportunistic mobile social networking, opportunistic marketing, and community service provision are further presented to demonstrate the potential application areas and technical solutions of opportunistic IoT.