As the deployment of Internet-of-Things (IoT) devices in intelligent systems from smart health to smart city expands, ensuring sensor data reliability is critical for accurate decision-making. Individual sensor data suffer data drift due to unwanted noises, faults and false data injections during deployment, which paves the way for exploiting inter-sensor data correlation to improve anomaly detection. Studies suggest that sensor readings are correlated in many applications. Existing methods employ data correlation among sensors in pre-processing steps but do not integrate it into the bedrock of the Kalman Filter (KF) state transitions. Such integration will provide a way to detect anomalies in dynamic and complex applications. This paper proposes a novel, two-stage anomaly detection system named BL-DKF to tackle these challenges. First, an innovative 2D dynamic KF (DKF) is introduced that incorporates inter-sensor data correlation for adaptive estimation through noise and fault/false data injections. DKF dynamically adjusts its process covariance matrix by analyzing correlation deviations, reducing its influence when the anomalies cause correlation drops to emphasize internal predictions. The estimates produced by DKF are fed to a compact Bidirectional LSTM (BL) model in the second stage, which analyzes anomalous patterns. We evaluate BL-DKF on two real-world sensor datasets, including the data collected from our IoT lab testbed. Results demonstrate that BL-DKF robustly detects nonlinear and physical anomaly injections, outperforming existing methods with an average of 9.36% detection accuracy improvement across four experiments. The proposed system presents an analytical solution to embed inter-sensor correlations into KF, advancing its effectiveness in many application domains.

Image sensors deployed in the Internet of Things (IoT) generate vast volumes of digital images. These images may be subject to deliberate alteration, compromising their trustworthiness. Estimating the trustworthiness of this image data is crucial for many applications; however, this aspect has not been adequately explored in the existing literature. In this article, we propose a robust and real-time trust estimation framework for IoT image data, leveraging numeric data generated from other types of sensors deployed in the same Area of Interest (AoI). The theoretical model was developed using statistical approaches, and Shannon’s entropy was employed to measure the uncertainty associated with sensor readings during a specific event. Later, we applied Dempster-Shafer theory (DST) of combination to fuse information collected from image as well as numeric data-generating sensors where both types of sensors were observing the same event in the same AoI concomitantly. To evaluate the proposed framework, we implemented an IoT testbed using LoRa sensor nodes, edge devices, an LoRaWAN gateway, the things network (TTN), and a data analytics server. The testbed was used to collect observation data of a fire event using image and temperature sensors in an indoor residential setup in different conditions. Consequently, eight data sets (four authentic and four hacked) were built, each containing both image and temperature data readings under various scenarios. The proposed trust framework accurately estimated the trust score of images (91% overall accuracy) across all the data sets and outperformed existing trust models.

Since diverse noises and irregularities impact on sensor data, self-declaration of sensor data reliability is crucial for advancing Internet of Things applications and industrial automation. Relevant works on reliability include sensor self-attribution of data confidence, and self-diagnosis of sensor faults using temporal data redundancy or neighboring sensor data. Models are built on edge devices and then transferred to sensors. Overall, the existing methods are computationally expensive, require real-time data from other sensors and incur considerable transmission overhead. Therefore, they are not suitable for independent sensor data reliability assessment. Addressing these issues, we introduce an independent reliability self-declaration method for sensors. Two Kalman filter-inspired, block-based lightweight algorithms are designed that handle isolated and burst noises and estimate block data reliability. Moreover, a conceptual model to dynamically adjust block size is proposed leveraging noise level and maximum TCP/IP packet size to reduce data transmissions. The reliability levels are conveyed using TCP header reserved bits to avoid communication overhead. The approach was tested using water quality monitoring (WQM) and healthcare application datasets. Results show, for burst noise, our lightweight and scalable approach attains superior accuracy in WQM (89.06%) and healthcare (82.63%) for five-level reliability estimation. A real-world deployment using an Arduino-based sensor node demonstrates the feasibility of the approach for in-sensor operation.

Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

Research in texture feature approximation is still in the embryonic stage because of difficulties in developing a sound theoretical model to express the unique pattern in the intensity-variation of pixels in the neighbourhood of the pixel-of-interest so that it can sufficiently discriminate different textures. Local texture descriptors are widely used in image segmentation as they comprise pixel-wise features. The Weber local descriptor (WLD) with differential excitation and gradient orientation components, inspired by Weber's Law, has been leveraged in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique. However, WLD has inherent drawbacks in the formulation of the components that limit its discriminatory capability. This paper introduces a novel texture descriptor by directly modelling the distribution of intensity-variation in the parametric space of the Weibull distribution using its shape and scale parameters. A unified ‘joint scale’ texture property is introduced, which can discriminate textures better than the individual parameters while keeping the length of the descriptor shorter. Additionally, the accuracy of WLD's gradient orientation component is improved by using an extended Sobel operator and expressing gradients in$[-\pi /2,\pi /2)$range. When incorporated in ICM, the proposed texture descriptor has consistently outperformed WLD and a recent enhancement with radial mean WLD (RM-WLD) on three benchmark datasets. It has also outperformed two other texture segmentation techniques and their deep learning based improvements.

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization.

Trustworthiness in an online environment is essential because individuals and organizations can easily be misled by false and malicious information receiving from untrustworthy users. Though existing methods assess users' trustworthiness by exploiting Twitter account properties, their efficacy is inadequate because of Twitter's restriction on profile and tweet size, the existence of missing or insufficient profiles, and ease to create fake accounts or relationships to pretend as trustworthy. In this paper, we present a holistic approach by exploiting ideas perceived from real-world organizations for trust estimation along with available Twitter information. Users' trustworthiness is determined by considering their credentials, recommendation from referees and the quality of the information in their Twitter accounts and tweets. We establish the feasibility of our approach analytically and further devise a multi-objective cost function for the A* search to find a quasi-optimal path between the trust evaluator and the user whose trustworthiness is being evaluated. We also propose an incentive mechanism to increase user participation in the trust evaluation process, and a threat model and trustworthiness measure of referees to thwart the possibility of providing an untruthful recommendation to inflate one's trustworthiness. The efficacy of our proposed approach is validated through experiments using Twitter data and extensive simulation in various scenarios.

To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster-Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles.

The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car.

Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique.

Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-by-step methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements.

Internet of Things (IoT) applications are increasingly being adopted for innovative and cost-effective services. However, the IoT devices and data are susceptible to various attacks, including cyberattacks, which emphasizes the need for pervasive security measure like trust evaluation on the fly. There exist several IoT numerical data trustworthiness measures which are based on the quality of information (QoI) and correlations. The QoI measurement techniques excessively exploit heuristics, while the correlation-based approaches predict temporal correlation using an average or moving average, which limits their efficacy. To improve accuracy and reliability, we propose a model for assessing trust of IoT sensor numerical data by representing the temporal correlation using temporal relationship. We represent the temporal relationship between data within a time window in two ways: first, using the discrete cosine transform (DCT) coefficients of daily data; and second, to obtain the impact of shuttle variation, we further divide the daily data into some time windows and calculate the average of each DCT coefficient over all time windows. These two feature sets are then used to develop two independent deep neural network models. The model outcomes are fused by the Dempster-Shepard theory to calculate trust scores. The strength of our model is evaluated using both trustworthy and untrustworthy data-the former are collected from sensors under controlled supervision in a smart city project in Melbourne, Australia and the latter are generated either by simulating breached sensors or perturbing real data. Our proposed approach outperforms a contemporary correlation-based approach in terms of trust score accuracy and consistency.

Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents' demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7 ∼ 32%) delivery performance.

The challenging but intriguing problem of modeling opinion formation dynamics in online social networks (OSNs) has attracted many researchers in recent years because the inherent complexities present in human opinion update process are yet to be clearly understood. Although the existing works adopt the distance-based homophily principle to model the neighbors' influences on the formation of an agent's opinion, they ignore several other key factors that govern the update process. Explicitly, we consider two essential aspects of the real-world opinion formation process that were not explored previously. First, we consider the predisposition of agents that leads to selective exposure to information when presented with different opinion sources. Second, we explicitly consider an agent's past interaction experience with others and how opinions encountered in the past interactions influence future opinion update process of that agent. Although the confidence level of an agent on the expressed opinion was previously used to distinguish an expert, we propose the concept of the relative credibility of the opinion sources for such distinction. For this, we take into account an agent's perceived credibility about others and the relative nature of human judgment when exposed to many opinion sources with different credibility. In addition, for the first time, the credibility of sources external to an OSN is considered in the opinion formation model proposed in this paper. We validate our model by analyzing its performance in capturing the real-world opinion formation dynamics using traces collected from an OSN, specifically Twitter. On the other hand, through simulation, various scenarios are created to observe the steady-state outcomes of the dynamics under various influences of our model parameters and network characteristics. Finally, different compelling and practical applications with social and economic values can be built based on our model.

Mass participation of the members of a society in discussions to resolve issues related to a topic leads to forming public opinion. The timeline of the underlying dynamics goes through several distinguishable phases, and experiences transition from one to another. After initiated by concerned individuals, it draws active attention from almost everyone, and with time progression, people's participation starts declining as the issues are resolved or lost attraction. The existing works in the literature to capture the opinion formation process pay attention to model the dynamics in its active phase and thus ignore the other phases and the corresponding phase transitions. Trust relationships among the participants dynamically shape their interactions in different stages of the dynamics. Existing works fail to incorporate trust in defining the extent of influence one has on others, as they define the social relationships in the opinion space. To address this issue, we adopt simulated annealing to model the transitional behaviour of the dynamics, and then, amalgamate peoples relationships in the trust space with that in the opinion space to define the meta-heuristics of the algorithm for capturing the dynamical properties of the process. Finally, through simulation, we observe that our model is insightful in representing peoples' evolving behaviour in the different stages of opinion formation process, and consequently, can capture the various properties of the steady-state outcomes of the dynamics.

Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach.

Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships.

A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.

Opinion formation in social networks has changed in a more rigorous way due to the inception of Online Social Networks (OSNs) as a platform of generating and sharing huge amount of contents as well as easy and ubiquitous access to varied information sources. Our opinions are not only updated through interactions with our neighbours in OSNs, but also shaped by the opinions received from information sources external to the native OSNs. Current models only consider the neighbours' influence in opinion evolution, thus lack the impact of other information sources, e.g., news media, Web search, bulletin board, discussion forum on opinion formation. They consider individual opinion distances to model the influence among interactive neighbours, but fail to capture the influence of majority supported opinions and its possible impact in opinion evolution. Our model explicitly captures the effect of external sources on opinion formation in an OSN. We combine the implication of most perceived opinions in terms of consistency along with opinion distance to emulate the influence of different opinion sources. Consistency is measured by the entropy of opinions derived from a particular source type. Simulation results show that our model properly captures the consensus, polarization and fragmentation properties of opinion evolution. Finally, we investigate the influence of stubborn agents on opinion formation and compare it with a contemporary model.

Smart mobile devices have become immensely popular among the people worldwide and provide a new platform for generating and sharing contents. The centralized and hybrid architectures for content sharing require constant Internet connection, increase traffic and incur costs. To address these issues several content sharing approaches have been proposed using the decentralized architecture. Most of the proposed approaches uses patio-temporal regularity and pre-existing social relationships of the users to predict their movements and facilitate content sharing. However, there are scenarios such as visiting hotspots where regular movement patterns or established social relationships among people might not exist. Content sharing in such scenarios has not been addressed yet in literature and existing prediction based approaches are ineffectual. This study focuses on facilitating content sharing in the afore-mentioned scenarios. We take account of user interests, recommendations from online social networks, hotspot specific activities and other relevant information to construct communities which facilitate content sharing. For each community an administrator, who maintains content and member lists and render directory services, is selected based on stay probability, interest score, battery lifetime and device configuration. Simulation results show that our proposed approach attains high content hit and success rate and low latency in delivery which is nearly comparable to those proposed for scenarios with regular predictable movement patterns reported in literature.

In the exclusive-use model of spectrum trading , cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer’s reliability, which the seller can derive from the buyer’s reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively.

Reliable data transfer for underwater acoustic sensor networks (UASNs) is a major research challenge in applications such as pollution monitoring, oceanic data collection, and surveillance due to the long propagation delay and high error rate of the acoustic channel. To address this issue, an opportunistic data forwarding protocol was proposed which achieves high packet delivery success ratio with less routing overhead and energy consumption by selecting the next hop forwarder among a set of candidates based on its link reliability and data transfer reach ability. However, the protocol relies on fixed data hold time approach, i.e., Each node holds data packets for a fixed amount of time before a forwarder discovery process is initiated. Depending on the value of the fixed hold time and deployment contextual scenario, this may incur large end-to-end delay. Moreover, lack of consideration of network condition in hold time limits its performance. In this paper, we propose an adaptive technique to improve its performance. The adaptive approach calculates data hold time at each node dynamically considering a number of 'node and network' metrics including current buffer occupancy, delay experienced by stored data packets, arrival and service rate, neighbors' data transmissions and reachability. Simulation results show that compared with fixed hold time approach, our adaptive technique reduces end-to-end delay significantly, achieves considerably higher data delivery and less energy consumption per successful packet delivery.

Secondary service providers can buy spectrum resources from primary service providers for a short or long period of time and exploit it to solve the problem of spectrum scarcity. This buying decision of spectrum buyers can depend on several factors including pricing of the spectrum, reputation of a seller, and duration of the contract and spectrum quality. However, existing pricing models for dynamic spectrum access consider mainly bandwidth which makes them unsuitable for real-world trading. In this paper, we consider these issues related to the pricing of spectrum sale in terms of microeconomic theories. First, we consider reputation of spectrum sellers and update it dynamically by considering a buyer's own trading experience with the sellers and collecting recommendations on sellers from other buyers. Second, trustworthiness of recommenders as well as incentive to encourage recommendations are modeled. Third, contract duration and spectrum quality are incorporated such that a buyer's utility is formulated as a function of buyer's resource requirement, reputation of seller and trustworthiness of recommenders. Fourth, the model is analyzed using dynamic pricing of the market and the solution is obtained using market equilibrium. Results demonstrate the superiority of our model over the existing microeconomic models for dynamic spectrum trading.

Designing message forwarding protocols for underwater acoustic sensor networks (UASNs) is challenging mainly due to high propagation delay, limited bandwidth and high packet loss. Most such protocols operate on the assumption that precise location of sensor nodes is known, which is difficult as GPS waves cannot propagate through water. Moreover, due to the error-prone nature of the acoustic link, message forwarding over multiple hops degrades end-to-end reliability, consumes significant energy and incurs longer delay. In this paper, we propose a location unaware message forwarding technique. It employs opportunistic routing where nodes use accumulate-and-forward paradigm to route data. The technique also exploits nodes' ability to overhear one another's transmission to select reliable route. Our opportunistic model uses independent and local forwarding decisions to select next hop forwarder on-the-fly based on its link transmission reliability and reachability to the gateway. Message ferrying approach is utilized to collect sensor data from gateway nodes of multiple UASNs at high data rate. Our simulation results exhibit its effectiveness and superiority compared with two well established message forwarding algorithms in underwater in terms of packet delivery ratio, routing overhead and energy consumption.

We develop a realistic pricing modeling for dynamic spectrum access network considering a number of factors that influence real world trading. Our model incorporates the reputation of sellers, incentive mechanism to attract buyers, timing requirement of the contract, signal quality along with bandwidth size and price of the spectrum resources. Reputation information is accumulated from self experience and/or from the recommendations of other users. Trustworthiness of the recommending SUs is also modeled in this paper. An incentive mechanism is applied to encourage in dissemination of recommendations and to attract the buyers by providing discounts. A utility function for the spectrum trading is modeled such that a buyer can trade with the seller which maximizes its utility based on the above factors and the pricing solution is obtained using the market equilibrium model. The performance of the proposed model is evaluated using simulation results which show that our model benefits both the sellers and the buyers in terms of profit and throughput, respectively.

Wireless sensor networks are an important new technology for remote monitoring. How to organize the nodes in a network remains a core practical problem. Node clustering is the most popular technique to increase the energy efficiency of a wireless sensor network, but the number of clusters greatly influences two performance metrics - data reliability and energy efficiency. Present methods treat these two metrics separately. To address this gap, we here introduce a joint optimal clustering technique (JOC), which optimizes both the number of clusters and the clustering process by considering intra and inter-cluster communication cost, link quality and traffic congestion so that both reliability and energy efficiency are maximized. We used simulation to compare JOC with HEED, the most popular of current clustering techniques. The results show that JOC improves network lifetime and reliability significantly.

Traditionally, event centric Wireless Sensor Network (WSN) applications treat all events with equal importance, implicitly assuming that all events have same priority. However, in real world applications events may have different level of severity and sensitivity based on their cost of potential damage, occurrence location and frequency. Such applications demand that a detection scheme adopt differentiated treatment of events considering above criteria. Recent works proposed multi-modal sensor nodes for detection of different types of event in a single sensor network and mobile nodes for on-demand attendance of events. When a multi- modal WSN is deployed to monitor events of varied priority, major challenges lies to allocate resources and mobilize mobile nodes in an optimized way to maximize detection performance. We introduce the concept of varied priority and cost of mis-detection of events, and propose a detection scheme for multiple simultaneous events in a hybrid sensor network. Mobile nodes are mobilized through formulation of an optimization problem that maximizes the prioritized accuracy while minimizing detection delay. Theoretical and simulation results demonstrate that our scheme significantly outperforms other scheme that treats all events equally.

Timeliness in query response is the major quality metric for query processing in the real-time applications of Wireless Sensor Networks (WSNs). The structure of the query routing tree directly affects the whole query processing delay as it provides the path to forward a query to the relevant nodes and return the response to the sink. In the current literature, query routing structure is designed irrespective of the variation in query loads among the sensors. As a consequence, current schemes do not guarantee for the routing tree to provide a faster path to the sensors with higher query load. This motivates the current work to consider query load in constructing and self-reconfiguring the routing tree. In this paper, we present a query load-based spanning tree construction method that reduces the query response delay as well as energy consumption in query execution and provides query response with the best possible accuracy. Simulation results illustrate the efficacy of the proposed framework.

Traditionally the sliding window (SW) has been employed in vertex-based operational rate distortion (ORD) optimal shape coding algorithms to ensure consistent distortion (quality) measurement and improve computational efficiency. It also regulates the memory requirements for an encoder design enabling regular, symmetrical hardware implementations. This paper presents a series of new enhancements to existing techniques for determining the best SW-length within a rate-distortion (RD) framework, and analyses the nexus between SW-length and storage for ORD hardware realizations. In addition, it presents an efficient bit-allocation strategy for managing multiple shapes together with a generalized adaptive SW scheme which integrates localized curvature information (cornerity) on contour points with a bi-directional spatial distance, to afford a superior and more pragmatic SW design compared with existing adaptive SW solutions which are based on only cornerity values. Experimental results consistently corroborate the effectiveness of these new strategies.

Spectrum resources are becoming more and more congested as the number of wireless devices are increasing and becoming ubiquitous. Cognitive radios or secondary users (SUs) can provide the solution for better spectrum availability, bandwidth and economic aspects for both the primary service providers and the SUs. We propose a pricing model for spectrum sharing in a single level market where the primary service providers can trade spectrum with the secondary service providers. The proposed pricing model incorporates the reliability of the primary service providers and allowable coverage area, quality of the signal along with the pricing and spectrum bandwidth availability. An iterative distributed algorithm is used to reach the market equilibrium so that both the primary and the secondary service providers are satisfied with the allocated spectrum bandwidth and negotiated price. The performance of the proposed model is demonstrated using extensive numerical results with the stability analysis in reaching the market equilibrium.

Existing in-network query processing techniques are categorized as approximation and aggregation based approaches, where the former achieves lower network traffic at the expense of query response accuracy, whereas the later reduces query response inaccuracy by executing queries at the actual sensor nodes which necessitates the overhead of query specific sensor selection mechanism. In this paper, we propose a hybrid query processing framework that combines the advantages of both the approximation and aggregation based techniques and avoids their limitations. In our approach, we construct a hierarchical probabilistic data model representing the overall sensor data characteristics across the network, which is query independent and is later used for selecting sensor nodes to process user queries. Experimental results illustrate the efficacy of the proposed framework compared to contemporary approximation and aggregation based query processing techniques.

Spectrum utilization in cognitive radio ad hoc network is a critical task due to the fluctuation of spectrum availability temporally and spatially. Reusing the same channel spatially can effectively improve the spectrum utilization as well as reduce the delay in switching and coordination in different channels. In this paper, a dynamic spectrum sharing method is proposed which allows multiple secondary users to reside in the same channel and use the channel concurrently to maximize the spectrum utilization exploiting variable transmission power and location information, while guaranteeing acceptable level of interference protection to the primary users. Results show that the proposed method successfully outperforms the existing method in maximizing the number of concurrent transmissions as well as the spectrum utilization.

For cost effective deployment and implementation, mobility is introduced in sensor networks to provide dynamic event coverage. A hybrid network of static and mobile nodes, can yield the same desired accuracy and robustness of a static k-coverage detection model with fewer nodes. Since node movement is a costly operation and the movement strategy has to be decided instantly after event occurrence, it is desirable to have a lightweight distributed node selection and movement scheme. In this work, we propose a game theoretic model to provide dynamic event coverage that achieves the desired detection accuracy with significantly fewer number of nodes while balancing the energy consumption due to mobility and keeping the travelling distance minimum. We address and exploit the spatial clustering nature of events to maximize the overall detection performance over the network lifetime.

Existing in-network query processing techniques are categorized as approximation and aggregation based approaches. The former achieves lower query response delay at the expense of accuracy, while the latter reduces query response inaccuracy by executing queries at the actual sensor nodes resulting in longer delay. In this paper, we propose a query processing framework which is delay as well as accuracy aware and capable of dynamic adjustment to meet user/application requirements. When query response is required within specific delay, it provides approximated sensor data meeting the delay requirement. On the other hand, when query response accuracy is vital, it tolerates longer delay in acquiring response with the desired accuracy. To achieve this, we propose a novel method of constructing a delay aware spanning tree (DAST) based on query load and organizing sensor data with varied accuracy. Experimental results illustrate superiority of the proposed framework against competing approaches.

One of the challenging aspects for wireless sensor network (WSN) clustering is to estimate the optimal cluster number. Most WSN clustering techniques have used an a priori value to determine the cluster number. A few techniques, available at the literature, attempted to find the optimal cluster number by energy minimization. However, due to the assumption of lossless communication, these initiatives do not always converge to the minimum energy as wireless channels are error-prone. As wireless communication is always vulnerable to the signal interference, environmental noise, and so on, it suffers from data loss. To capture the characteristics of the communication phenomenon in a real context and have better reliability, we need to embed the link quality into the cluster number determination process. To address this issue, in this paper together with the energy, we incorporate link quality metric into the theoretical underpinning to achieve an optimal cluster number analytically and finally justified the result by simulation.

Collision Sense Multiple Access (CSMA) has been preferred to Time Division Multiple Access (TDMA) as medium access scheme for Wireless Multimedia Sensor Network (WMSN) in the scenarios where the traffic is bursty in nature and multiple consecutive and contiguous packets generated from the same collision neighborhood need to be sent. Protocols based on nonuniform probability distribution do not perform well in high contention and heterogeneous traffic scenarios due to nonadaptive nature to contention neighborhood. In this paper we have proposed a scheme to adapt the Contention Window (CW) size according to the collision neighborhood population complying with the application specific latency and success probability constraints. This scheme shows improved performance compared with SIFT, a stereotype of non-uniform probability based CSMA protocol and can be deployed with any CSMA-CA (CSMA with Collision Avoidance) based backoff algorithm.

Cluster heads (CHs) form the backbone of the crucial inter-cluster communication for wireless sensor network (WSN). Due to relaying high data traffic, some of these nodes quickly exhaust their precious energy and increase the risk of node failure. In conjunction with the node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Traditional routing algorithms choose either minimum hop count or merely energy efficient paths to forward data towards the base station (BS), consequently, they don't directly consider the reliability (data loss) in the selection process of a routing path for a WSN. In this paper, we propose a cost function for routing path selection mechanism for inter-cluster communication by exploiting a trade-off between energy and reliability. We use link quality and congestion at CH node as path metrics related to reliability. Our protocol analyzes all possible inter-cluster communication paths between a source CH and the BS and selects minimum energy path, maximum reliability path and a trade-off between these two. Performance analysis of the proposed scheme is justified by simulation, which demonstrate that our technique achieves substantial energy savings and improved reliability over the contemporary cluster based routing techniques.

Position based routing protocols have lower routing overhead due to exploiting position information of mobile nodes for forwarding data. The performance of location based protocols depends on the precise knowledge of the destination's location. Therefore a location service is a prerequisite, from which a transmitter can find the approximate location of the receiver node. Several location service schemes have been proposed in literature, among them hierarchical services became attractive due to their scalability. These schemes adopt hash function based location server (home) assignment which requires nodes to be distributed throughout the concerned area uniformly. Node mobility in real world may cause non-uniform node distribution under which condition performance of the existing location schemes degrades considerably. This demands an improved location service scheme which can adapt itself with all contextual situations. In this paper we propose a novel location service scheme which performs better than existing location services in both uniform and non-uniform node distributions while maintaining scalability in location update and query.

Since shape is regarded as one of the most important attributes of visualisation, it plays a pivotal role in semantic video object segmentation applications. One of the major objectives for the research community is to segment specific objects of interest from a video sequence using prescribed shape descriptors in a diverse range of applications from video surveillance and object tracking through to medical imaging. This paper addresses this challenge by presenting a new probabilistic spatio-temporal (PST) video object segmentation algorithm that incorporates a priori generic shape descriptor representations of particular objects in a sequence. The algorithm provides considerable improvement in perceptual picture quality compared with the existing PST segmentation technique, with the numerical analysis corroborating the superior subjective segmentation performance achieved.

Embedding textural information into the probabilistic spatio-temporal (PST) video object segmentation is very important for achieving better segmentation, since this is one of the key perceptual attributes of any object. Existing video segmentation techniques however, ignore this feature because of the underlying difficulty in defining and hence characterizing a texture, which theoretically limits their segmentation performance. To address this problem, this paper proposes a new video object segmentation algorithm that involves a strategy to seamlessly incorporate texture information as a pixel feature in the PST framework. Experimental results for a variety of standard test sequences reveal a significant performance improvement in the quality of the video object segmentation achieved in comparison with the original PST method.

Existing vertex-based operational rate-distortion (ORD) optimal shape coding algorithms can use a number of different distortion measurement techniques, including the shortest absolute distance (SAD), the distortion band (DB), the tolerance band (TB), and the accurate distortion measurement technique for shape coding (ADMSC). From a computational time perspective, an N-point contour requires O(N2) time for DB and TB for both polygon and B-spline-based encoding, while SAD and ADMSC incur O(N) time for polygonal encoding but O(N2) for B-spline based encoding, thereby rendering the ORD optimal algorithms computationally inefficient. This letter presents a novel distortion measurement strategy based on chord-length parameterization (DMCLP) of a boundary that incurs order O(N) complexity for both polygon and B-spline-based encoding while preserving a comparable rate-distortion performance to the original ORD optimal shape coding algorithms

Existing vertex-based operational rate-distortion (ORD) optimal shape coding algorithms use a vertex band around the shape boundary as the source of candidate control points (CP) usually in combination with a tolerance band (TB) and sliding window (SW) arrangement, as their distortion measuring technique. These algorithms however, employ a fixed vertex-band width irrespective of the shape and admissible distortion (AD), so the full bit-rate reduction potential is not fulfilled. Moreover, despite the causal impact of the SW-length upon both the bit-rate and computational-speed, there is no formal mechanism for determining the most suitable SW-length. This paper introduces the concept of a variable width admissible CP band and new adaptive SW-length selection strategy to address these issues. The presented quantitative and qualitative results analysis endorses the superior performance achieved by integrating these enhancements into the existing vertex-based ORD optimal algorithms.

The identification of protein-protein interface is essential for proper annotation of protein-function, drug design and interpreting protein interaction networks. Desolvation properties of protein surface play an important role in protein-protein binding. We present a method here that uses desolvation energy to identify protein-protein interface. Utilizing desolvation energy, the optimal docking area (ODA) method in Fernandez-Recio, J. et al, (2005) identifies protein-protein interfaces by calculating the ODA values and then applying a fixed threshold on the ODA values for all proteins. The proposed method derives desolvation energy histograms of all proteins from ODA values and calculates an individual threshold for each protein to identify interface. An individual threshold for a test protein is calculated based on the ODA values of known hot spots of a protein that has the closest match to its ODA histogram with test protein. Results show that overall success rate improved to 58.8% from 39% on a dataset comprised of 51 proteins involved in non-obligate hetero-complexes. The proposed method predicted at least one hot spot in 49 cases as compared to 31 in the ODA method. In addition, comparable results were found for both X-ray and NMR structures

From a video object segmentation perspective, using a joint spatio-temporal strategy is superior to processing with priority in either the spatial or temporal domains, as it considers a video sequence as a spatio-temporal grouping of pixels. However, existing spatio-temporal object segmentation techniques consider only pixel features, which tend to limit their performance in being able to segment arbitrary shaped objects. To address this limitation requires a new strategy for embedding generic shape information seamlessly into the segmentation process and this paper presents a new shape-based probabilistic spatio-temporal algorithm that achieves this objective. Experimental results using a number of standard video test sequences reveal a considerable performance improvement in being able to segment arbitrary shaped video objects in comparison with other contemporary space-time based video segmentation methods

Existing shape-based clustering algorithms, including fuzzy k-rings, fuzzy k-elliptical, circular c-shell, and fuzzy c-shell ellipsoidal are all designed to segment regular geometrically shaped objects such as circles, ellipses or combination of both. These algorithms however, are unsuitable for segmenting arbitrary-shaped objects, so in an attempt to address this issue, a fuzzy image segmentation of generic shaped clusters (FISG) algorithm was introduced that integrated generic shape information into the segmentation framework. It however, had a number of limitations relating to the mathematical derivation of the updated contour radius, the initial shape representation, and the impact of overlapping clusters. This paper proposes a new object based segmentation using fuzzy clustering (OSF) algorithm that solves these drawbacks by controlling the scaling of original shape, securing a better initial shape representation and avoids cluster overlapping, with both qualitative and quantitative results confirming the improved overall segmentation performance

Maximum likelihood (ML) is a popular and widely used statistical method, and while it is effective, its major short-comings are that it is a biased and non robust estimator. This paper proposes a formal establishment of an optimisation of ML (OML) by approximating the true distribution minimising the bias, and exploiting the underlying relationship between ML and the maximum entropy method. OML exposes the inefficiency of the classical ML in the orthogonal least square error minimisation sense, for a number of finite sample datasets. The robustness of the proposed OML method in finding an estimate within the boundaries of the parameter space is also proven. Under the same conditions, OML consistently provides a more global and efficient estimation, so both theoretically and empirically establishing its superiority over ML in terms of efficiency and robustness

Embedding generic shape information into probabilistic spatio-temporal video object segmentation is of pivotal importance to achieving better segmentation, since it provides valuable perceptual clues for humans in both distinguishing and recognising objects. Recently a probabilistic spatio-temporal video object segmentation algorithm incorporating shape information has been proposed, though since it is restricted to only pixel features, the probability of a pixel belonging to a certain cluster is directly correlated with its spatial location, which theoretically limits the segmentation performance of the technique. To address this problem, this paper proposes a new probabilistic spatio-temporal video object segmentation algorithm that incorporates generic shape information based on its region. Experimental results reveal a significant performance improvement in arbitrary-shaped video object segmentation compared with other contemporary methods for a variety of standard video test sequences.

Existing vertex-based operational-rate-distortion (ORD) optimal shape coding algorithms use a fixed width admissible control point band (FCB) around the shape boundary as the search space for possible control points. The width of the band however, is fixed and arbitrarily chosen independent of the admissible distortion and shape contour, so it fails to fully exploit the admissible control point band to reduce the bit-rate. This paper proposes a variable width admissible control point band (VCB) where the width associated to each boundary point is dynamically determined from the admissible peak distortion and shape information. In addition, the paper uses an accurate distortion measurement method to overcome a key limitation of existing distortion and tolerance band based methods. Experimental results reveal that both the qualitative and quantitative performance of the existing ORD algorithms are improved by seamlessly integrating the VCB and accurate distortion measuring approach.

The Bezier curve is fundamental to many challenging and practical applications, ranging from computer aided geometric design and postscript font representations through to generic object shape descriptors and surface representation. A drawback of the Bezier curve however, is that it only considers global information about the control points, so there is often a large gap between the curve and its control polygon, leading to considerable error in curve representations. To address this issue, this paper presents enhanced Bezier curve (EBC) models which seamlessly incorporate local information. The performance of the models is empirically evaluated upon a number of natural and synthetic objects having arbitrary shape and both qualitative and quantitative results confirm the superiority of both EBC models in comparison with the classical Bezier curve representation, with no increase in the order of computational complexity.

The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability. This limitation was the primary motivation in our investigation into using shape information to improve the generality of these algorithms. Fuzzy shape-based clustering techniques already consider ring and elliptical profiles in segmentation, though most real objects are neither ring nor elliptically shaped. This paper addresses this issue by introducing a new shape-based algorithm called fuzzy image segmentation of generic shaped clusters (FISG) that incorporates generic shape information into the framework of the fuzzy c-means (FCM) algorithm. Both qualitative and quantitative analyses confirm the superiority of FISG compared to other shape-based fuzzy clustering methods including, Gustafson-Kessel algorithm, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters. The new algorithm has also been shown to be application independent so it can be applied in areas such as video object plane segmentation in MPEG-4 based coding.

Bezier curves (BC) are fundamental to a wide range of applications from computer-aided design through to object shape descriptions and surface mapping. Since BC only consider global information with respect to their control points, this can lead to erroneous shape representations, though integrating local control point information minimises this error. This paper presents a new dynamic Bezier curve (DBC) model which combines both localised and global shape information by making a parametric shift of the BC points in the gap between the curve and its control polygon. The value of the shifting parameter is dynamically determined for a prescribed maximum distortion. DBC retains the kernel properties of the BC without increasing computational complexity order. The model's performance has been empirically evaluated on a number of arbitrary-shaped objects from geometric modelling to shape coding. Both qualitative and quantitative results confirm the improvement achieved compared with the classical BC representation.

Results of any clustering algorithm are highly sensitive to features that limit their generalization and hence provide a strong motivation to integrate shape information into the algorithm. Existing fuzzy shape-based clustering algorithms consider only circular and elliptical shape information and consequently do not segment well, arbitrary shaped objects. To address this issue, this paper introduces a new shape-based algorithm, called fuzzy image segmentation using shape information (FISS) by incorporating general shape information. Both qualitative and quantitative analysis proves the superiority of the new FISS algorithm compared to other well-established shape-based fuzzy clustering algorithms, including Gustafson-Kessel, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters.

Most fuzzy rule-based image segmentation techniques to date have been primarily developed for gray level images. In this paper, a new algorithm called fuzzy rule-based colour image segmentation (FRCIS) is proposed by extending the generic fuzzy rule-based image segmentation (GFFUS) algorithm G.C. Karmakar, L.S. Dooley [2002] and integrating a novel algorithm for averaging hue angles. Qualitative and quantitative analysis of the performance of FRCIS is examined and contrasted with the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms for both the hue-saturation-value (HSV) and RGB colour models. Overall, FRCIS provides considerable improvement for many different image types.

The extended fuzzy rules for image segmentation (EFRIS) algorithm initially splits all segmented regions into mutually exclusive 4-connected objects, from which the largest one in each region is designated as its main object. A drawback of this approach is that it is less effective when the main objects are relatively small and some of the minor objects are completely surrounded and connected to the main object of another region. Besides, defining insufficient merging rules, EFRIS also only considers the surrounding main objects in the original order that the regions were segmented, which is undesirable. In this paper, a new general segmentation algorithm called modified extended fuzzy rules for image segmentation (MEFRIS) is presented, which addresses these problems and whose improved segmentation performance is analysed and numerically evaluated. The results are also contrasted with both the original generic fuzzy rule-based image segmentation (GFRIS) and EFRIS algorithms.

The generic fuzzy rule-based image segmentation algorithm (GFRIS) does not produce good results for images containing non-homogeneous regions, as it does not directly consider texture. In this paper a new algorithm called fuzzy rules for image segmentation incorporating texture features (FRIST) is proposed, which includes two additional membership functions to those already defined in GFRIS. FRIST incorporates the fractal dimension and contrast features of a texture by considering image domain specific information. Quantitative evaluation of the performance of FRIST is discussed and contrasted with GFRIS using one of the standard segmentation evaluation methods. Overall, FRIST exhibits considerable improvement in the results obtained compared with the GFRIS approach for many different image types.

Many fuzzy clustering based techniques do not incorporate the spatial relationships of the pixels, while all fuzzy rule based image segmentation techniques tend to be very much application dependent. In most techniques, the structure of the membership functions are predefined and their parameters are either automatically or manually determined. This paper addresses the aforementioned problems by introducing a general fuzzy rule based image segmentation technique, which is application independent and can also incorporate the spatial relationships of the pixels. It also proposes the automatic defining of the structure of the membership functions. A qualitative comparison is made between the segmentation results using this method and the popular fuzzy c-means (FCM) applied to two types of images: light intensity (LI) and an X-ray of the human vocal tract. The results clearly show that this method exhibits significant improvements over FCM for both types of images.

The generic fuzzy rule-based image segmentation (GFRIS) technique does not produce good results for non-homogeneous regions that possess abrupt changes in pixel intensity, because it fails to consider two important properties of perceptual grouping, namely surroundedness and connectedness. A new technique called extended fuzzy rules for image segmentation (EFRIS) is proposed, which includes a second rule to that defined already in GFRIS, that incorporates both the surroundedness and connectedness properties of a region's pixels. This additional rule is based on a split-and-merge algorithm and refines the output from the GFRIS technique. Two different classes of image, namely light intensity and medical X-rays are empirically used to assess the performance of the new technique. Quantitative evaluation of the performance of EFRIS is discussed and contrasted with GFRIS using one of the standard segmentation evaluation methods. Overall, EFRIS exhibits significantly improved results compared with the GFRIS approach.