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.

In recent years, the Internet of Things (IoT) has penetrated all aspects of our lives through smart cities, health, industries and others that are related to people's livelihood. With the increasing number of IoT devices, more and more personal information is exposed in the network space, which inevitably brings some network security problems. Due to the diversity and heterogeneity of IoT devices, identification of such devices in the complex IoT environments remains a major challenge. Existing deep learning-based device identification methods achieve identification of IoT devices by automatically extracting device traffic features, but usually only single modal features of device traffic are considered, which cannot achieve all-around characterization features of communication traffic and affect the identification results. Therefore, we propose an identification method, termed DMRMTT, that employs a Deep convolutional maxout network and MTT model (Multiple Time-series Transformers) to automatically extract the spatial and temporal features of IoT communication session fingerprints and perform further fusion using the structure of the residual, which makes up for the limitations of the existing methods for studying device traffic. This method can improve the characterization of device traffic behaviour and achieve a more accurate identification of IoT devices. Its efficacy is experimentally validated by using two publicly availbale datasets and compared with existing methods. Results show that our method outperforms other methods in widely used performance metrics and achieves 99.82% identification accuracy, demonstrating its superiority and usefulness in IoT device identification.

Abstract Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment.

Mobile edge computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above-mentioned problems, in this article, the task offloading problem is formulated as an optimization problem, and particle swarm optimization (PSO) and quantum PSO based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared with recent advanced strategies, namely ant colony optimization, multiagent deep deterministic policy gradients, deep meta reinforcement learning-based offloading, iterative proximal algorithm, and parallel random forest.

The Underwater sensor network (UWSN), also known as Marine Sensor Network (MSN), is gaining increasing attention due to its applications in the monitoring of the marine environment and assisting Marine Intelligent Transportation Systems (MITS). Such systems provide in-vehicle assistance services (i.e., traffic monitoring and driver alerts) by gathering transportation and environmental information. Though very promising, there are several barriers to developing energy-efficient communication protocols for heterogeneous MSN, including selecting optimal routing paths twinned with the lifetime of these sensor nodes along the path, which are restricted due to the limited energy storage capacity. Hereby, the selection of an optimal route path also necessitates harvesting and management of the sensor nodes’ energy. To facilitate this, the current work presents REER-H, a Reliable Energy Efficient Routing protocol with Harvesting for cluster-based MSN capable of multi-source energy harvesting and an incorporated energy management technique. Incorporating three separate layers of the protocol stack, namely, network, MAC, and physical layers, REER-H uses its proposed adaptive scheduling technique to support collision-free data transmission by assigning adaptive time slots based on demand and data load. Also, the proposed integrated energy harvesting and management solves the energy hole problem and enhances the overall network lifetime. In comparison to the existing cooperative and cluster-based energy-efficient routing protocols for underwater maritime communication, the simulated results using Network Simulator-3 (NS3) reveal that the proposed scheme remarkably enhances the overall network performance in terms of packet delivery ratio, throughput, lifetime energy consumption, and end-to-end delay for MSN.

Resilience is an increasingly important concept in current socio-economic landscapes. Due to the competitive global context and security attacks, the organisations are looking for realistic resilience assessments for operations of their digital networks. This study proposes a node Operational Resilience evaluation based on the fuzzy logic by assessing various cyber security dynamics; including node threat protection, avoiding degradation, attack identification and recovery vectors. Through extensive experiments and analysis, we reached to a better understanding of diverse relationships between cyber security factors for the evaluation of Operational Resilience.

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.

In this paper, we consider the problem of information theoretic security for a single-input single-output (SISO) relay-aided network in the presence of an adaptive eavesdropper. We assess the impact of deceptive friendly jammers on the secrecy of communication in this network when countering adaptive eavesdroppers. Specifically, we derive the secrecy capacity and secrecy outage probability of the network and compare the results in the absence and presence of a deceptive friendly jammer. Our results show that the secrecy capacity of the network increases while the achievable secrecy outage probability decreases significantly in the presence of friendly jammer to nullify the effect of the adversary. Numerical results, obtained through computer simulations, under different scenarios of varying jamming power and average main channel gain to average eavesdropper channel gain ratio demonstrate the effectiveness of friendly jammer in providing physical layer security.

Mobile malware detection is inherently an imbalanced data problem since the number of benign applications in the market is far greater than the number of malicious applications. Existing methods to handle imbalanced data, such as synthetic minority over-sampling, do not translate well into this domain since mobile malware detection generally deals with binary features and these methods are designed for continuous features. Also, methods adapted for categorical features cannot be applied here since random modifications of features can result in invalid sample generation. In this work, we propose a novel technique for generating synthetic samples for mobile malware detection with imbalanced data. Our proposed method adds new data points in the sample space by generating synthetic malware samples which also preserves the original functionality of the malicious apps. Experiments show that the proposed approach outperforms existing techniques in terms of precision, recall, F1score, and AUC. This study will be useful in building deep neural network-based systems to handle imbalanced data for mobile malware detection.

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.

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes. It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.

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.

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.

In machine health monitoring, fault frequency identification of potential bearing faults is very important and necessary when it comes to reliable operation of a given system. In this paper, we proposed a data mining based scheme for fault frequency identification from the bearing data. In this scheme, we propose a compact tree called SAP-tree (sliding window associated frequency pattern tree) which is built upon the analysis of frequency domain characteristics of machine vibration data. Using this tree we devised a sliding window-based associated frequency pattern mining technique, called SAP algorithm, that mines for the frequencies relevant to machine fault. Our SAP algorithm can mine associated frequency patterns in the current window with frequent pattern (FP)-growth like pattern-growth method and used these patterns to identify the fault frequency. Extensive experimental analyses show that our technique is very efficient in identifying fault frequency over vibration data stream.

We present a computationally efficient RGB-D based pose estimation solution for less computationally resourced MAVs, which are ideally suited as members in a swarm. Our approach applies the sufficient statistics derived for a least-squares problem to our problem context. RANSAC-based outlier detection in aligning corresponding feature points is a time consuming operation in visual pose estimation. The additive nature of the used sufficient statistics significantly reduces the computation time of the RANSAC procedure since the pose estimation in each test loop can be computed by reusing previously computed sufficient statistics. This eliminates the need for recomputing estimates from scratch each time. A simpler hypotheses testing method gave similar performance in terms of speed but less accurate than our proposed method. We further increase the efficiency by reducing the problem size to four dimensions using attitude data from an Attitude and Heading Reference System (AHRS). Using a real-world dataset, we show that our algorithm saves up to 94% of computation time for the RANSAC-based procedure in pose estimation while improving the accuracy.

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.

Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-tree is enhanced into PShrFSP-tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.

Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.

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.

To ensure customer satisfaction and greater market acceptance, voice over Wi-Fi networks must ensure voice quality under various network parameters, configurations and traffic conditions, and other practical effects, e.g., channel noise, and capturing effects. An accurate voice capacity estimation model considering these factors can greatly assist network designers. In the current work, we propose an analytical model to estimate voice over Internet Protocol (VoIP) capacity over Wi-Fi networks addressing these issues. We employ widely used ITU-T E-model to assess voice quality and VoIP call capacity is presented in the form of an optimization problem with voice quality requirement as a constraint. In particular, we analyze delay and loss in channel access and queue, and their impacts on voice quality. The proposed capacity model is first developed for a single hop wireless local area network (WLAN) and then extended for multihop scenarios. To model real network scenario closely, we also consider channel noise and capture effect, and analyze the impacts of transmission range, interference range, and WLAN radius. In absence of any existing call capacity model that considers all the above factors concomitantly, our proposed model will be extremely useful to network designers and voice capacity planners.

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.

WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.

Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.

Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available datasets, WATF achieves higher fault detection accuracy, even when training and testing datasets belong to different severity levels.

Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.

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.

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.

Complex target tracking applications require active sensor nodes to collaboratively track multiple moving targets, which can balance the trade-off between the quality of tracking and network's lifetime. In this paper, we develop a distributed sensor-selection protocol (DSSP) to activate dynamic number of sensors based on the cost metrics. Cost metrics contains energy-aware leadership cost and eagerness-based tracking cost; which selects sensors with higher energy resources and information utilities. DSSP enables an even distribution of energy consumption among the nodes to prolong the network lifetime. Our results show that the proposed scheme can significantly improve the network lifetime while maintaining the high tracking accuracy as compared to the other schemes.

Efficient target tracking applications use active sensor nodes collaboratively to track multiple moving targets by balancing the trade-off between the quality of tracking and network's lifetime. In this paper, we propose a low-energy dynamic sensor selection (LEDS) scheme to track multiple targets by estimating energy consumption of sensors and information utility projection of the targets on sensors to calculate the eagerness in tracking. Eagerness represents the eligibility of a sensor node to be selected for tracking, considering relative profiles of other sensors and location of all the targets in its vicinity. LEDS enables an even distribution of energy consumption among the nodes to prolong their remaining energies. Our results show that the proposed scheme can significantly improve the network lifetime over the existing methods while maintaining the high tracking accuracy in congested areas where multiple concurrent targets overlap.

Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.

In a WLAN-cellular integrated network scenario, the most important point of consideration is the vertical handoff strategy applied for WLAN admission control. The admission control has previously been based on the parameters like congestion level in WLAN network. We propose a novel approach for handoff to WLAN by using connectivity graph data from online social networking services. Stronger social ties with other users advocate a higher probability of admission to WLAN. The main benefit of proposed handover strategy is diminution in global social network traffic. Simulation results prove the validity of our proposed approach against conventional methods.

Energy efficiency is a vital aspect of resource constrained wireless sensor networks (WSNs). All protocols designed for WSNs must be energy aware in order to prolong the network lifetime. In this paper, we have designed a novel MAC layer protocol (I-MAC: Intelligent MAC) for WSNs. By exercising intelligent sleep and wake-up schedule, I-MAC saves energy of the resource constrained sensor nodes greatly. At the same time, I-MAC does not compromise its operational performances. Both analytical study and simulation prove that I-MAC is not only highly energy efficient but also its operational performances are better than similar protocols.

Wireless sensor networks (WSNs), now-a-days, are deployed in environmental data collection as well as in critical event monitoring. Successful data collection requires reliability while reliable event detection necessitates timeliness. Simultaneous data gathering and event monitoring is not well studied in literature. In this paper, we propose a system model that works on homogeneous data gathering WSNs. When an event occurs, an event cluster with a different transmission channel is formed and both data gathering and event monitoring are performed at the same time. The proposed model has a novel routing strategy with a built-in congestion control technique to provide timely delivery of event data. Experimental results show that the proposed method performs better than known similar techniques in terms of reliable data gathering and reliable timely event monitoring. It also enhances the network lifetime significantly compared to other existing methods.

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.

One of the most challenging issues in cognitive radio networks is to dynamically access the radio frequency spectrum in an uninterrupted manner. To achieve this, omniscient allocation of spectrum bands among cognitive radio users is crucial. Most of the existing spectrum allocation methods select a band from a pool according to the service requirements of a single user, neglecting the demand of multiple users. In this paper, we introduce a collaborative framework for allocating multiple bands among multiple secondary users. The proposed method defines a capacity of service metric based on the optimal sensing parameters and utilizes this metric to assign distinct bands to all or highest possible number of contending users. Performance evaluation suggests that the proposed method exhibits significant superiority over conventional approaches in terms of improved throughput and spectrum utilization, reduced interference loss and collision, and hence, enhances dynamic spectrum access and sharing capabilities.

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.

Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by the WiFi technology. While WiFi enjoys a high penetration in the market, its hotspots are connected to the internet through wired connections, making its deployment cost very high. WiMAX has emerged as an existing new wireless technology, which provides larger coverage and higher bandwidth. Deployment of WiMAX only infrastructure, however, is highly expensive, which has motivated researchers to search for a low cost integrated WiMAX/WiFi architecture (using WiMAX as the backhaul and WiFi as the last mile technology) that supports 4G applications and provides high speed broadband services. WiMAX technology is equipped with mechanisms capable of delivering guaranteed quality of service (QoS). WiFi, on the other hand, has very limited capacity for providing QoS to the end applications. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. In this paper, we depict a converged architecture of WiMAX and WiFi, and then propose an adaptive resource distribution model for the access points. The new model is designed as an optimization problem that maximizes the QoS utility of the network. A new QoS utility function is proposed that takes the connection priority and continuity into account. Our simulation results show that our proposed scheme maintains QoS in different scenarios whereas existing other resource sharing schemes experience violation of QoS (minimum rate requirement) in 66% cases.

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.

Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.

Cellular networks coupled with wireless local area networks would be very common in the next- generation wireless environments. This integration would be made possible by employing techniques of vertical handover across different radio access networks. Finding precise timing to carry out vertical handover is an important problem and is the topic of this research work. We propose a hybrid vertical handoff (HVH) initiation by intelligent fusion of adaptive threshold, signal trend detection and variable width dwell timer. Simulation studies have shown that our proposed approach results in a better vertical handoff experience as compared to existing techniques.

Transmission policy, in addition to topology control, routing, and MAC protocols, can play a vital role in extending network lifetime. Existing transmission policies, however, cause an extremely unbalanced energy usage that contributes to early demise of some sensors reducing overall network's lifetime drastically. Considering cocentric rings around the sink, we decompose the transmission distance of traditional multihop scheme into two parts: ring thickness and hop size, analyze the traffic and energy usage distribution among sensors and determine how energy usage varies and critical ring shifts with hop size. Based on above observations, we propose a transmission scheme and determine the optimal ring thickness and hop size by formulating network lifetime as an optimization problem. Numerical results show substantial improvements in terms of network lifetime and energy usage distribution over existing policies. Two other variations of this policy are also presented by redefining the optimization problem considering: 1) concomitant hop size variation by sensors over lifetime along with optimal duty cycles, and 2) a distinct set of hop sizes for sensors in each ring. Both variations bring increasingly uniform energy usage with lower critical energy and further improves lifetime. A heuristic for distributed implementation of each policy is also presented.

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.

Wi-Fi enabled hand-held devices quickly occupied the consumer market as a result of the remarkable customer acceptance of IEEE 802.11 standard. But the widely used contention based medium access mechanism is unable to meet the increasing traffic demand of today's home users. Especially, delay sensitive multimedia contents suffer heavily from quality degradation under high traffic load. However, the time synchronized medium access mechanism of 802.11 called PCF offers lower delay and loss, and holds true potential in supporting high traffic load. For design and planning of such networks, a thorough performance analysis of PCF based 802.11 WLANs is of profound importance. But existing studies on this lack proper and accurate mathematical modeling considering realistic assumptions, which is investigated in this paper. We develop a Markov chain to analyze the time synchronized PCF based medium access mechanism considering error prone channels, and determine throughput, delay, and packet loss in a WLAN. The impact of traffic load and retry limit is also analyzed. We use our model to analyze performance of PCF mechanism in supporting both data and multimedia traffic.

Voice capacity over IEEE 802.11 networks can be increased with time synchronized medium access, but it is restricted to single hop networks only. We propose a collaborative mechanism that enables client nodes to carry out the time synchronization on ad hoc basis, thereby extends the time synchronized access mechanism over multiple hops, and increases voice capacity considerably. To model real scenario closely, we consider the impact of channel error and employ a Markov chain to determine delay and loss in both medium access and in queue which is then used to derive user perceived voice quality using ITU-T E-model. Our model is then used to determine voice capacity of multichannel, multihop networks. The impact of data rate, interference range, and multiple channels are also analyzed.

Wireless local area networks (WLANs) offer a promising role in the fourth-generation heterogeneous wireless networks. This requires efficient and timely switching of a mobile node's connection (called vertical handover) from cellular network to WLAN. Existing methods to initiate vertical handover do not fully utilize the WLAN potential and result in switching to cellular networks even when a WLAN network is available. We propose a hybrid approach to determine vertical handover timing with a goal to maximize the utilization of WLAN resources, while maintaining a low probability of handover failure. Simulation results indicate the proposed technique showing better performance in terms of number of ping-pong events and handover dropping probability as compared to existing techniques which are based on mean RSS, FFT and adaptive threshold.

Increase in the number of coexisting networks in license free Industrial, Scientific and Medical (ISM) band causes interferences for industrial automation, e.g., shop floors of manufacturing facilities. In order to ensure the reliability for automation networks, interference avoidance schemes are required. This paper proposes a novel Predefined Hopping Pattern (PHP) technique for frequency hopping in ISM band, which mitigates self-interferences and static interferers as well. This technique generates optimized frequency hopping sequences which ensure sufficient frequency diversity and frequency offset among the coexisting Bluetooth piconets and exploits transmission experiences for a particular frequency in eliminating interference. Simulation studies have shown that PHP has better collision avoidance rate than well known adaptive frequency hopping (AFH) and adaptive frequency rolling (AFR) schemes.

Increase in the number of coexisting networks in Industrial, Scientific and Medical (ISM) band cause interferences and demands for intelligent interference avoidance schemes. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets which has the tendency to mitigate both the self and static interferences and ensures sufficient frequency diversity. Simulation studies validate the prospects for the proposed scheme to be used for frequency hopping networks against already existing techniques, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR).

One of the most important aspects of cognitive radio technology is to avoid interference on the primary system. Typically, the interference is avoided by sensing a particular spectrum band for the existence of primary transmitter while all secondary users are kept quiet. Hence, a periodic sensing method is used which incorporates alternate phases of sensing and transmission by all secondary users. In this paper, we introduce a new method of agile spectrum evacuation that allows any secondary user to continue using the band until the return of the primary user is detected through the formation of a set of users that exclusively engages in sensing primary user in a cooperative manner. The proposed method yields better interference protection and enhanced spectrum utilization.

The quick market growth and ubiquitous acceptance of VoIP are primarily due to cheap service. VoIP services can be availed in mobile devices at a very low cost by employing IEEE 802.11 to provide last mile coverage. But call capacity is very low in these networks and call jitter occurs if voice quality requirements are not precisely met. We present a VoIP call capacity model for 802.11 networks which considers the most important real world factors like power capture and imperfect channel. Incorporating multiple channels can increase call capacity while multihop network can provide considerably large coverage. Our capacity model incorporates the effects of both multiple channels and multihop networks. We also consider the availability of multiple radio interfaces and conference call scenarios for ubiquitous applications of our model. To ensure voice quality, in addition to contention analysis, we also model the queue considering collision domain at each hop and formulate call capacity by estimating precise impairment budget over multihop.

Successful event detection in Wireless Sensor Networks (WSN) requires reliability and timeliness. When an event occurs, the base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the event, and the data sent by other nodes have little importance. In this paper, we propose Congestion Avoidance and Mitigation (CAM) protocol that performs the function of a routing protocol as well as the function of a transport protocol. During routing, it avoids congestion by avoiding congested routes. It also mitigates congestion by utilizing an accurate data-rate adjustment when congestion occurs. Experimental results show that CAM is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.

Protection of interference on the primary system is the key requirement for deployment of a cognitive radio system. Typically, spectrum sensing is interleaved in the transmission process of a secondary user to detect the return of primary user for quick evacuation, resulting in frequent stopping of transmission. In this paper, we introduce a novel method of agile spectrum evacuation that ensures continuous transmission by the secondary user until the primary user actually returns. The sensing process goes on simultaneously by a dedicated set of users working in a cooperative manner. The proposed method is evaluated under fading and shadowing conditions and better interference protection and improved utilization of RF spectrum is obtained.

Trade-off between energy-efficiency and reliability in wireless sensor networks is application dependent. Without the reliability, the extended lifetime of a network is of limited use. Due to the inherent correlation between reliability and congestion, it is necessary to reduce congestion to improve reliability. Existing congestion control algorithms in wireless sensor networks are reactive. They attempt to reduce the congestion only after its detection. In this paper, we present Location Aided Congestion Aware Routing (LACAR) protocol that proactively avoids congestion formation and improves data delivery success rate in data gathering wireless sensor networks. Location, energy and congestion information of neighbours together with the location information of the base station determine appropriate routes. Simulation results show that LACAR achieves high packet success rate in an energy-efficient way.

The IEEE 802.11 standard offers a cheap and promising solution for small scale wireless networks. Due to the self configuring nature, WLANs do not require large scale infrastructure deployment, and are scalable and easily maintainable which incited its popularity in both literature and industry. In real environment, these networks operate mostly under unsaturated condition. We investigate performance of such a network with m-retry limit BEB based DCF. We consider imperfect channel with provision for power capture. Our method employs a Markov model and represents the most common performance measures in terms of network parameters making the model and mathematical analysis useful in network design and planning. We also explore the effects of packet error, network size, initial contention window, and retry limit on overall performance of WLANs.

VoIP offers cheap voice communication which instigated its quick market growth. IEEE 802.11 WLANs offer cheap wireless coverage which can be used to provide VoIP service in mobile devices. But call capacity of such networks is very low compared to wired networks. To increase voice capacity in WLANs incorporation of power capture, increased data rate, and use of multiple channels are of great importance. Moreover, these networks should be carefully designed considering voice quality requirements to avoid call jitter and call drops. We propose an analytical model to estimate VoIP call capacity for multichannel WLANs with consideration for power capture, imperfect channel, and multiple network interfaces. We employed ITU-T E-model to assess voice quality which is used as the limiting factor to ensure that voice quality does not degrade in such networks. The model will be extremely helpful to network designers in planning single channel or multi-channel WLANs.

Fixed spectrum licensing policies are unsuitable to meet RF spectrum demand by emerging technologies. Spectrum scarcity is hindering the enhancement of current services or deployment of new services operating in RF spectrum. On the contrary, many portions of the licensed spectrum remain unused or underused for significant period of time creating possibility of opportunistic spectrum access without license. Spectrum sensing is practiced in most of the existing opportunistic spectrum access methods. However, these methods consider identical fading or shadowing channels and ignore spatial variation of unlicensed users, hence fail to capture realistic scenario. We introduce an improved cooperative spectrum sensing technique which incorporates non-identical fading and shadowing, and weighs each non-licensee user's contribution appropriately considering the received power and positional displacements of the users. The proposed method demonstrates better detection accuracy and higher spectrum utilization with fewer cooperating users than other existing methods.

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.

In recent years, research on VoIP over wireless mesh network (WMN) has gained particular attention because of its commercial prospect. This paper presents an analytical method to estimate VoIP call capacity in an WMN employing IEEE 802.11 devices. We used Markov chain analysis of IEEE 802.11 for network delay and loss to estimate the capacity while using rating factor, R score, defined by ITU-T, to ensure call quality. A detailed analysis of queueing delay and loss in terms of network parameters is also carried out along with their impacts on voice quality. The capacity model estimates call capacity in a single hop WLAN and is extendable to multi hop scenario and for video communications. The theoretical results are verified by simulation and compared to related previous works.

Most existing data models and transmission policies for sensor network assume uniform periodic data generation and unconstrained transmission range for sensor nodes, both assumptions being too restrictive to capture and analyze real- world operation for practical deployment. In this paper, we consider these two practical aspects and present a new transmission policy formulated after (i) stochastic data model where a set of events occur with certain probabilities and rate of data generation by a sensor varies based on sensed event and (ii) limited transmission range of sensors. Assuming co-centric rings around the base station, located at a generic location (internal or external to the network area), ring thickness and hope sizes over lifetime is determined by formatting an optimization problem where nodes in each ring may transmit data at different hop sizes at a given instant and also vary hop sizes over lifetime. Performance analysis shows significant improvement in network lifetime and better uniformity in energy usage distribution in the proposed policy irrespective of network size and maximum allowable transmission range of nodes.

For many applications, simultaneous sensing of a number of parameters is crucial that leads to the deployment of multiple classes of sensors having different initial energy, data generation rate and deployment density within the vicinity of a cluster as opposed to identical sensors assumed in the existing heterogeneous sensor networks. For data transmission to cluster head, such networks use single hop, multi hop and their hybrid as intra-cluster transmission policy which suffer highly from non-uniform energy usage among sensors, thereby reducing the lifetime drastically leaving considerable amount of energy in many nodes. In this paper, we propose a framework for multi-class heterogeneous sensor networks where incoming traffic is relayed towards cluster head in collaboration among multiple classes of sensors considering their heterogeneity. We also propose two transmission policies for this framework considering generic polygonal cluster and limited transmission range for individual sensors. Performance analysis shows substantial improvement of overall lifetime by the collaborative framework of multi-class sensors. Our proposed transmission policies further improve the lifetime over existing multi hop and hybrid communications through better distribution of energy usage among sensors.

Although transmission policy is crucial in extending lifetime of sensor networks, most existing policies make simplified assumptions which include: i) circular cluster with cluster head (CH) at the center, ii) uniform periodic data generation model and iii) unrestricted transmission range for nodes. But, in practice, these assumptions are too restrictive for real-world deployment of heterogeneous sensor networks where clusters are usually polygonal. Moreover, in multi hop transmission energy consumption by sensors varies greatly with their distance from CH and even among sensors in the critical ring due to non-uniform relay traffic caused by asymmetric polygonal structure of cluster. In this paper, we propose a new transmission policy where sensors transmit at optimally determined hop sizes that varies over lifetime and a distributed hop selection algorithm that regulates each packet's arrival to CH within a given hop limit. Our formulation considers generic polygonal cluster, stochastic data generation model where data generation rate by sensors vary with events and limited transmission range for sensors. Performance analysis shows significant improvement in lifetime and better uniformity in energy usage among sensors in the proposed policy irrespective of cluster size, hop limit and maximum allowable transmission range of nodes.

Non-uniform energy usage among sensors due to varying distances from the base station makes some nodes to die early, thereby reducing the network's lifetime drastically leaving considerable amount of residual energy in others. Performance optimization techniques that have recently been proposed include optimal transmission policies and dynamic relocation of the base station within the network area. Here we propose two transmission policies, namely, fixed hop size (FHS) and synchronous variable hop size (SVHS) transmissions considering generic location for the base station. We decompose the transmission distance of traditional multi hop protocol into two parts: ring thickness and hop size, and determine the optimal values of these parameters by formulating network lifetime as an optimization problem. Numerical results show that each of our policies perform substantially better in terms of network lifetime and energy usage distribution than the single hop and multi hop transmission policies irrespective of network size and distance of the base station from the network centre.

This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between IR and book-ahead (BA) call connections. BA reservation, which confirms the availability of resources in advance, is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns for the implementation of BA reservation is the need for preemption of on-going IR calls to accommodate BA calls when resource scarcity arises. Preemption disrupts service continuity of on-going calls which is considered as severely detrimental from users' perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No works have yet focused on the preemption problem of on-going IR calls at routing stage in the presence of BA calls. We present a mathematical formulation to compute the preemption probability of an incoming IR call at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of the calculated preemption probability of the incoming IR call and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms widely recommended shortest path and widest path routing algorithms in terms of IR call preemption and blocking rate. The proposed approach also yields higher network utilization and IR effective throughput.

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

Secondary structure, residue contacts and contact numbers play an important role in tertiary structure determination of proteins. In the recent past, mainly due to non local interactions, the Bayesian segmentation approach has been successfully used for secondary structure prediction. In this paper, the performance of the Bayesian segmentation approach has been enhanced by taking residue contacts into account. The three state prediction accuracy increased by 2% when residue contacts were taken into account. Due to the inherent flexibility the Bayesian segmentation approach has been extended to infer residue contacts and contact numbers with the same segmentations. The proposed method achieved CorRvalues greater than 0.70 for protein sequence 1a62 and 1aba

Preemption is an efficient technique to provide available and reliable services to high priority connections in a QoS-enabled network. Preemption technique is governed by a preemption policy which makes the decision about which connections to preempt when resource scarcity is experienced. Previously proposed policies considered preemption rate, priority of connection and preempted bandwidth as the deciding criteria for preempting connections. In this paper, another new criterion, namely, user satisfaction is introduced in formulating the objective function that defines the preemption policy as an optimization problem. Simulation results show that the proposed policy incorporating user satisfaction outperforms the existing preemption policy in terms of customer satisfaction with a significant margin and performs comparably in terms of preemption rate and priority of preempted calls. The improved user satisfaction indicates higher prospects of revenue return and make the proposed policy highly attractive to the network providers.

This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between instantaneous request (IR) and book-ahead (BA) call connections. Book-ahead reservation which confirms the availability of resources in advance is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns in the implementation of BA reservation is the preemption of on-going instantaneous requests (IR) call connections. Preemption disrupts service continuity which is seen as detrimental from users' perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No work known to these authors has yet focused on the preemption problem of on-going IR call connections at routing stage. We present a mathematical formulation to compute the preemption probability of an IR call connection at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of calculated preemption probability of incoming IR call connection and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms both shortest path and widest path routing algorithms in terms of preemption and call blocking rate.

Preemption of low priority call connections to accommodate high priority call connections is a widely recommended technique in a multimedia communication network. A preemption policy is required to make decision about which connections to preempt when resource scarcity is experienced. In literature, priority of connection, preempted bandwidth and the number of preempted connections are proposed as the three basic criteria governing a preemption policy. So far revenue prospect of a communication provider, specially in relation to consumer satisfaction, has not been incorporated in preemption policy. In this paper we introduce revenue index, a metric that indicates the level of estimated consumer satisfaction, as an additional criterion to be used in conjunction with the above three. Revenue index is an indicative of long term revenue prospect of an enterprise. We present formulation of the preemption policy as an optimization problem. A heuristic to approximate the optimal solution is also derived. Simulation results show that the proposed model of preemption policy when adopted by a network provider ensures better consumer satisfaction for the end users which leads to higher revenue index for the network provider

In an erroneous footfall ground-reaction force-time recording, which may occur for people with disabilities or frail elderly individuals, the stance time (ST) can be either corrupted or missing. Previous methods to estimate missing ST require force-time data from multiple force platforms and are affected by inter-step variability. This paper presents a model based on support vector machine (SVM) that is capable of estimating the missing ST from the available vertical force-timing characteristics with significantly high accuracy. The model was built using features taken from a data set of 466 sample trials of 27 subjects. A test on 40 sample trials drawn from all the subjects revealed an average prediction accuracy of 96.63% (/spl plusmn/2.89%). In one-fourth of the test trials, the prediction error was within 1.0%. The model achieves considerable improvement over an artificial neural network based model built and tested on the same data set. The effect of kernel junction parameters and /spl epsiv/-insensitive loss function on prediction error is also analysed and presented.

Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and /spl epsiv/-insensitive loss function. We investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented.

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.

An analysis of the testability of a class of low capacitance full-swing BiCMOS logic circuits is presented in this paper. It is shown that the stuck-open faults in the bipolar drivers of these circuits are masked by the additional MOS devices used to obtain full output logic swing. However, the stuck-open faults in the MOS devices are detectable by two-pattern tests as in standard CMOS. All single stuck-on faults result in significant increment in I/sub DDQ/ when sensitized. Therefore, like static CMOS, these faults can be detected by I/sub DDQ/ testing.