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Social Opportunistic IoT (Social OppIoT) networks is a subclass of Social Internet of Things (SIoT) networks. In social OppIoT, users perform communication in a distributed manner using smart devices by regularly moving around without any communication infrastructures, making routing a strenuous process due to its highly fragile connection intermittency and device mobility. Moreover, due to the growing heterogeneous devices, problems can exist in searching for the right relay node from a massive number of devices. In this paper, a novel forwarding scheme named “A Local Betweenness Centrality Based Forwarding Technique for Social Opportunistic IoT Networks” (LBCFT) has been proposed, which uses a reduction strategy to discard the inefficient devices. LBCFT introduces a new centrality measurement called local betweenness centrality to form the significant overlapping communities of network devices to boost forwarding. Message dissemination is controlled by handling inefficient devices using a reduction strategy, which includes the node’s trajectory and intra-community and inter-community centrality. The performance of LBCFT is evaluated through ONE Simulator against the existing analogous ideologies like Supernode, Geo-Routing with Angle-based Decision (GRAD), and the benchmark protocols BubbleRap, as well as PROPHET. The simulation results show that the proposed LBCFT protocol, on average, outperforms Supernode, GRAD, BubbleRap, and PROPHET by 5.22%, 36.51%, 64.12%, and 57.96% respectively, in terms of the delivery probability.

Wireless sensor networks sense events, collect data and forward it to the infrastructural node, called sink, for further processing and assessment. The communication from sensor node to the sink is an energy consuming task. Clustering is one of the strategies to provide communication in the sensor network. Maintenance of the clusters and re-clustering are important to improve connectivity of the network. An efficient clustering protocol reduces the energy consumption in addition to providing connectivity and communication path, because energy is a scarce commodity in wireless sensor network. The following paper proposes a clustering procedure to reduce energy consumption, reduce delay in communication and improve the connectivity of the network. The proposed algorithm aims to improve the performance and lifetime of the network by improving the above mentioned parameters.

In opportunistic networks (OppNets), messages are transmitted between nodes in an opportunistic fashion following a store-carry-and-forward approach and a routing protocol, with the hope to reach their destinations. In existing routing protocols for OppNets, different parameters such node’s delivery predictability, node’s mobility, distance with respect to destination, node’s energy consumption, contact duration between nodes, to name a few, are considered in the selection of the best suitable next hop to carry the message toward the destination. This paper proposes a novel routing protocol that uses three parameters, namely the contact duration, the node’s residual energy and the signal- to-noise-plus-interference-ratio (SINR) simultaneously to decide on the selection of the best next forwarder for a message. Simulation results using the Opportunistic Network Environment (ONE) simulator are presented, demonstrating the effectiveness of the proposed technique.

Integrating social networks properties such as centrality, tie strength, etc into message forwarding protocols in opportunistic networks has grown into a vital major benchmark. The opportunistic network is a demanding network with no set route to travel a message from the source to be able to the destination. During these networks, nodes use possibilities gained based on store-carry-forward patterns to forward communications. Every node that obtains a message when it activities another node makes selection concerning the forwarding or not necessarily delivering the node came across. Most of these message forwarding protocols use the benefit of social properties information like contact information and social relationship enclosed by the nodes in the social opportunistic network. In this paper, a Bonding based forwarding technique is proposed which is finding direct and indirect bonding among nodes by exploiting contact information and social pattern. In the proposed protocol, we also focus on indirect bonding by finding weakest direct bonded nodes and then replace it with strong indirect bonded nodes of the network. In this work, the balance between transmission delay and network traffic is considered by using shortest path map based mobility model. ONE simulator is used for simulation and performance of the proposed protocol is compared contrary popular approaches for instance Epidemic, PRoPHET, and BubbleRap, and Interaction based when using the shortest path map based mobility model. The Bonding based forwarding technique performs adequately well concerning the number of messages delivered, overhead ratio, message dropping and average latency.

In Social opportunistic networks where no end to end path exists between a sender and a destination node, the nodes usually perform message routing using the store-carry and forward pattern. Nodes consume a significant amount of energy during the node discovery phase and the message transmission phase. Therefore, it is challenging to design an energy-efficient message forwarding protocol. This paper improves an existing scheme named bonding based technique for message forwarding in social opportunistic network (BBFT) concerning energy consumption. The newly introduced energy-efficient BBFT approach proposes an energy estimation model that estimates the amount of energy consumed for transmitting, receiving, and scanning in the message forwarding process by the sender node. This energy consumption estimation model reduces the message flooding in the network, which results in conservation in the nodes’ residual energy, hence increasing the network’s lifetime. Simulation has been performed using ONE simulator to assess the EBBFT algorithm’s performance on energy parameters such as average residual energy, dead nodes, and other parameters overhead and dropped messages and compared against the BBFT and the energy-efficient SEIR protocols. On average, EBBFT is 90.65\(\%\) better than BBFT and 27.18\(\%\) better than SEIR concerning average residual energy while varying the message generation interval and had no dead nodes.

In the Social opportunistic networks (SONs), nodes inspect the transmission area concurrently for data forwarding and discover relevant relay neighbors to accomplish effective message transmission. The context information of the nodes like distance, contact duration, encounter, centralities, tie strength, etc. are analyzed to find out right relay nodes that support in designing an efficient routing protocol. Developing such kind of routing protocols that optimize multiple conflicting objectives is computationally strenuous. This paper proposes a Non-dominated sorting Genetic algorithm-II based routing protocol for social opportunistic Networks (NSRP). NSRP uses NSGA-II for optimizing the node’s average forwarding delay degree, and the collaboration probability objective functions to get non-dominated solutions for message passing. This proposed protocol is robust and expected to produce high delivery ratio by optimizing multiple parameters of the SONs.

Designing an efficient routing strategy in terms of energy is a great challenge in OPPNETs as there is no static network topology, which results in high mobility in the node's aspect. In search of most liable and scalable node for communicating the data packets from source to candidate node or directly to destination, lots of energy is required. To maintain the node's stability in the network, the energy parameter is worked on. This paper presents the energy efficient version of the A*OR known as Energy Proficient A * Search Based Hop Selection Routing Perception in infrastructure-Less Opportunistic Networks: EA * OR which not only saves the energy but also selects the prominent node from such network where the connections are intermittent in nature. EA*Or's experimental results outperforms E-PRoPHET, E-PRoWait, E-EDR and ELPFR-MC with respect to average energy left, number of dead nodes, delay in messages, messages delivered, drop rate of messages and overhead ratio. In terms of node's residual battery life EA*Or performance is far better than E-PRoPHET by 25%, 78% better than E-PRoWait, 74% better than E-EDR and 68% better than ELPFR-MC as count of nodes varies.

In recent times, it has been observed that opportunistic networks (OPPNET) are attracted by many researchers. The reason behind it is that no end-to-end connectivity exists and still the messages are communicated from the source to destination in this network. This is because, this network uses the node’s mobility for routing and hence selection of mobility models are considered very crucial while evaluating the movement of the nodes in any routing protocol. Appropriate selection of a mobility model is important for evaluating the performance of the protocol and secondly, to substantiate its applicability in the real-world scenario. This chapter analyzes the impact that three specific mobility models (namely SPMBM, RWP, and RTT) have on the performance of the existing A*OR protocol for OppNets using average hop count, delivered messages, number of message dropped, overhead ratio, and average latency as performance metrics. A suitable environment is set up using ONE simulator for simulating the A∗OR protocol over different movement models. Experimental results show significant variations in the performance of A∗OR for the SPMBM, RWP, and RTT. In terms of delivery predictability, the SPMBMM model is 80% better than RWP and 90% than RTT. The average latency of the SPMBMM model is 16% better than RWP and 59% than RTT when TTL varies in the network.

In today's world due to ever increasing demand to make computers perform tasks of humans, machine learning is used. It is a tedious task to manually read the entire book and classify it based on its genre. Novice writers find it troublesome to figure out the genre of their book, which can affect its reach to the right audience. The proposed method gains knowledge from a large number of words from the books and transforms them into a feature matrix. During transformation, the size of the initial matrix is reduced using Wordnet and Principle Component Analysis. Then, AdaBoost classifier is applied to predict the genres of the books.

Data collected in Wireless Sensor Network is transmitted to the sink by the sensor nodes. Sink processes the data for further action. These networks are energy constrained and hence communication to sink should be energy efficient. Clustering has been proved to be an effective strategy in WSNs. Member nodes of cluster collect data and send it to their respective cluster heads. It is now the duty of cluster heads to transmit data to the sink. Routing is done during multi-hop transmission to select an optimal route to the sink. The proposed algorithm maintains the clustering in the mobile network and saves the overhead of re-clustering. It is expected to provide better connectivity and performance of the system. Later the performance of proposed clustering is analyzed when different routing protocols are used for the path selection.

Data warehouse is the most important asset of an organization as it contains highly valuable and sensitive information that is useful in decision-making process. The data warehouse provides easy access to organizational data as it contains data from different sources. Thus, it is essential to structure security measures for the protection of data that resides in data warehouse against malicious attackers to ensure proper security and confidentiality. Security should be considered vital from initial stages of designing data warehouse and hence should be deployed. Though a lot of work is being done towards the improvement and development of data warehouse till now but very less attention is given on the implementation of the security approaches in data warehouse. This paper focuses on improving the level of security by combining One-Time Pad (OTP) encryption technique with Advanced Encryption Standard (AES) to encrypt the data before loading it into data warehouse. Finally, the proposal of the model is to incorporate OTP encryption technique in the architecture of data warehouse for enhancing its security.

In the current scenario data security has become an important issue with the growth of digital media. Many users and the applications are accessing the data both from inside and outside the database. Hence, the database as well as data within these databases has become the key target for most of the attackers. Many cryptographic schemes have been designed to solve this problem. Encryption plays an important role in providing the data confidentiality to data stored within the databases. But, the problem in adopting the standard encryption methods is that they may cause a damage to the existing schema as well as to the underlying applications or database as the output length is different from the input length and it also changes the format of data. This paper proposes a Format Preserving Encryption method by accumulating with Advance encryption standard(AES), eXclusive OR operation and a translation method for 16 digit numeric data. Format preserving encryption technique is used to minimizes the databases changes by preserving the format as well as the length of the input data.

Wireless sensor networks comprise of sensor nodes which collect data by sensing events and forward the data to sink or base station. Transmitting data to the base station is an energy-consuming job and needs optimal routing for ensuring less data loss and delay. Lifetime and performance of the network can be enhanced by balancing the load and energy consumption during routing. Changing the deployment environment also calls for new routing paradigm to attain desired results. Various routing techniques have been proposed for terrestrial and underwater wireless sensor networks. In this paper, we have comparatively analyzed the existing routing techniques and identified the present drawbacks and future challenges. This study will be helpful in designing new algorithms which will alleviate the existing drawbacks and result in better performance of the system.

One of the most used networks is the wireless sensor network. Such networks sense the environment and collect data when an event occurs. This data is transmitted to the base station, called the sink for analysis and decision regarding any action required. Sensor nodes sense events and forward data to the sink. This transmission is an energy consuming task and sensor networks are generally energy constraint. Hierarchical approach of using cluster heads has proven to be a good method to deal with this energy crisis and results in better performance of the network in terms of connectivity and communication. Routing algorithm chooses an optimal route from the sensor nodes to the sink for actual data delivery. This paper introduces a mobile relay node in the clustering scheme to enhance the performance of the network by maintaining the clusters and reducing the re-clustering overhead.

To retrieve appropriate information from large image datasets, Content Based image retrieval (CBIR) is a popular approach. In this paper we use binary clustering simultaneously on target and query images to retrieve color difference. One also measure geometric spreadness of each color, using coordinate information of clusters and used it with color difference with some weighted. Experimental results show that CBIRC gives better result as compare to other binning methods and ACE.

Wireless networks are suspectible to many attacks, including an attack known as the wormhole attack. The wormhole attack is very powerful, and preventing the attack has proven to be very difficult. A strategic placement of the wormhole can result in a significant breakdown in communication across a wireless network. In such attacks two or more malicious colluding nodes create a higher-level virtual tunnel in the network, which is employed to transport packets between the tunnel endpoints. These tunnels emulate shorter links in the network and so act as benefit to unsuspecting network nodes which by default seek shorter routes. This paper present a novel trust-based scheme for identifying and isolating nodes that create a wormhole in the network without engaging any cryptographic means. With the help of extensive simulations, we demonstrate that our scheme functions effectively in the presence of malicious colluding nodes and does not impose any unnecessary conditions upon the network establishment and operation phase. An ad-hoc network is built, operated, and maintained by its constituent wireless nodes. These nodes generally have a limited transmission range and so each node seeks the assistance of its neighbouring nodes in forwarding packets . In order, to establish routes between nodes, which are farther than a single hop, specially configured routing protocol are engaged. The unique feature of these protocols is their ability to trace routes in spite of a dynamic topology. The nodes in an ad-hoc network generally have limited battery power and so active routing protocols endeavor to save upon this, by discovering routes only when they are essentially required. In contrast, proactive routing protocols continuously establish and maintain routes, so as to avoid the latency that occurs during new route discoveries. Both types of routing protocols require persistent cooperative behaviour, with intermediate nodes primarily contributing to

Wireless Sensor Network is a highly promising technique which is used for monitoring environmental conditions in marine areas. In marine areas it is very important to know the state of the sea conditions to direct gateways to be opened from the neighboring areas to allow water activities. Messages are sent from nearby sea areas stating the sea conditions. This paper proposes a novel framework for deployment of Wireless Sensor Networks in marine environment to carry out information retrieval and generate intelligent data. This method uses SentiWordNet, a lexical resource which is used for opinion mining. This framework extracts intelligent information from the aggregate message by first filtering the message and transforming it by lemmatization and then gathering the opinion words and calculating their scores with the help of SentiWordNet. Thereby polarity of the sentence is calculated and the message is conveyed. The proposed method is executed on the messages sent from various marine areas and the results prove the efficiency and accuracy of the method.

Opportunistic network (oppnet) is one of the challenging fields of wireless network. It is an intermittently connected mobile network where nodes are mobile and no end-to-end path exists. Connectivity among nodes is established when they are within the transmission range of each other. If a node has message to communicate and no intermediate node is available, then the message is stored in the node's buffer till an appropriate communication opportunity arises which is known as store-carry-forward paradigm. This paradigm has given Oppnets a new direction in the field of research. In any network, efficient and effective routing is very crucial. In this paper, a novel routing technique has been proposed for Oppnets named as A* based opportunistic routing (A*OR) which uses the A* searching technique to select the best forwarders towards the destination. The proposed protocol is compared with prophet, PRoWait and EDR as a benchmark protocols and is found to perform 19%, 11% and 32% better than prophet, PRoWait and EDR, respectively in terms of delivery probability.

A distinguishing characteristic of wireless sensor networks is the opportunity to exploit characteristics of the application at lower layers. This paper reports on the results of a simulation comparison of proposed data dissemination protocols using the J-Sim simulator for the WSN protocols: Forwarding Diffusion Data Dissemination(FDDDP), Decentralized Data Dissemination(DDDP), Credit Broadcast Data Dissemination (CBDDP), Energy Aware & Geographical Data Dissemination (EAGDDP) .Our performance provides useful insights for the network designer such as which protocols (and design choices) scale control traffic well, improve data delivery or reduce overall energy consumption,improves routing overhead and maximizes the bandwidth utilization. The static pre configuration of the cell size in DDDP, is one of the reasons why DDDP exhibits larger routing overhead than FDDDP by 74.2% on average. Although CBDDP produces approximately 94.6% smaller overhead than DDDP and 90.7% smaller than FDDDP, because of statically configured amount credit CBDDP delivers on average 7.5 times more of the redundant data packets than DDDP and FDDDP.EAGDDP improves the delivery by 80% on average and makes a balance of energy consumption .We suggest that making these protocols truly self-learning can significantly improve their performance., 8 pages, 9 figures

To enable data aggregation among the event sources in wireless sensor networks and to reduce the communication cost there is a need to establish a coveraged tree structure inside any given event region to allow data reports to be aggregated at a single processing point prior to transmission to the network. In this paper we propose a novel technique to create one such tree which maximizes the lifetime of the event sources while they are constantly transmitting for data aggregation. We use the term Centralized Lifetime Maximizing Tree (CLMT) to denote this tree. CLMT features with identification of bottleneck node among the given set of nodes. This node collects the data from every other node via routes with the highest branch energy subject to condition loop is not created. By constructing tree in such a way, protocol is able to reduce the frequency of tree reconstruction, minimize the delay and maximize the functional lifetime of source nodes by minimizing the additional energy involved in tree reconstruction., Comment: 6 pages

In recent time, Internet or network services has gain popularity due to rapid growth in information and telecommunication technologies. Internet or network services become the mean for finance management, education, and global information service center for news, advertisements and many others. Denial of service attack and most particularly the distributed denial of service attack (DDoS) is most common and harmful threat to the Internet or network services. In order to design and develop reliable and secure network services, rapid detection and quick response to these attacks are major concern. In practice, there is no scheme that completely detects or prevents the DDoS attack. Predicting number of zombies in a DDoS attack is helpful to suppress the effect of DDoS attack by filtering and rate limiting the most suspicious attack sources or improve DDoS response system. In this paper, we present machine learning approach based on support vector machine for regression to predict the number of zombies in a DDoS attack. MATLAB implementation of support vector machine for regression and datasets generated using NS-2 network simulators running on Linux platform are used for training and testing. SVM for regression with various kernel function and other parameters are compared for their prediction performance using mean square error (MSE). Results show SVM based scheme have promising prediction performance for small dataset.

At present, distributed denial of service attack (DDoS) is most common and harmful threat to Internet infrastructure. Many approaches are given in literature for handling these attacks; however there is no scheme that can completely prevent or detect these attacks. Estimating strength of a DDoS attack in real time is helpful to suppress the effect of a DDoS attack by filtering or rate limiting the most suspicious attack sources. In this paper, we present artificial neural network (ANN) based scheme to estimate strength of a DDoS attack. Datasets generated using NS-2 network simulator running on Linux platform are used for training and testing feed forward neural network. Feed forward neural network with different number of neurons are compared for their estimation performance using mean square error (MSE). Simulation results show proposed scheme can estimate strength of DDoS attack in real time efficiently.

Wireless sensor networks consists of resource limited sensor nodes and resource-rich nodes, called sink nodes, of higher capabilities to perform powerful actions. In this paper, we focus on data delivery systems to transfer packets when required. Since sensor nodes are usually equipped with limited energy sources and are likely to be influenced by unpredictable failures occurring in the harsh sensor field, we need to seriously take fault tolerance and energy-efficiency into consideration. Hence, we develop a fault tolerant optimal path (FTOP) scheme to construct a maximum number of node-disjoint paths to deliver data packets in event notification with minimum energy consumption. We also evaluate FTOP performance by simulations and compare it with a related scheme to show its advantages.

Wireless Sensor networks are non-infrastructure networks which consist of mobile sensor nodes. Since the mobile nodes have limited battery power, it is very important to use energy efficiently in sensor networks. In order to maximize the lifetime of sensor networks, traffic should be sent via a route that can avoid nodes with low energy thereby minimizing the total transmission power. In addition, considering that the nodes of sensor networks are mobile, on-demand routing protocols are preferred for sensor networks. In this paper, we propose a novel on-demand power aware routing algorithm supporting quality of service called QDPRA. QDPRA prolongs its network lifetime by compromising between minimum energy consumption and fair energy consumption without additional control packets. QDPRA also improves its data packet delivery ratio, minimizes delay and maximizes throughput of the network.

Networking in Wireless Sensor networks is a challenging task due to the lack of resources in the network as well as the frequent changes in network topology. Although lots of research has been done on supporting QoS in the Internet and other networks, but they are not suitable for wireless sensor networks and still QoS support for such networks remains an open problem. In this paper, a new scheme has been proposed for achieving QoS in terms of packet delivery, multiple connections, better power management and stable routes in case of failure. It offers quick adaptation to distributed processing, dynamic linking, low processing overhead and loop freedom at all times. The proposed scheme has been incorporated using QDPRA protocol and by extensive simulation the performance has been studied, and it is clearly shown that the proposed scheme performs very well for different network scenarios.

Providing real-time data delivery in wireless sensor networks is a challenging research problem. In this paper we propose a centralized control plane incorporating the timed token protocol in the MAC layer for providing real-time data delivery in wireless sensor networks. In this approach hard real time guarantee is provided by a dynamic ring structure, where high priority stations have more chance of admittance and stations with low priority can be removed from the ring. Soft real time guarantee is provided by using proactive wireless routing protocol (DSDV) for path finding and maintenance, timely delivery of data is done through a prior bandwidth reservation. Simulation results show that the proposed control plane and bandwidth reservation ensures higher priority traffic more bandwidth than lower priority traffic and guarantees real-time data delivery.