Md Zakirul Alam Bhuiyan - Academia.edu (original) (raw)

Papers by Md Zakirul Alam Bhuiyan

Research paper thumbnail of Security and Attack Vector Analysis of IoT Devices

Security, Privacy, and Anonymity in Computation, Communication, and Storage

Research paper thumbnail of An Anonymous Protocol with User Identification and Linking Capabilities for User Privacy in a Permissioned Blockchain

Electronics

A permissioned blockchain includes a user in the network after verifying the user’s identity, in ... more A permissioned blockchain includes a user in the network after verifying the user’s identity, in contrast to Bitcoin, which is a public blockchain that allows network participation without third-party approval. The two types of permissioned blockchains are private blockchains, each consisting of one server and multiple users, and consortium blockchains, which consist of groups of private blockchains. However, a blockchain has privacy issues, such as user tracking and inference. Therefore, cryptography should be applied for user privacy in a blockchain. There is a lot of research on anonymous protocols for privacy in a blockchain. In this paper, we provide a scheme for user management, i.e., identification and authorization, in a permissioned blockchain. We also propose an anonymous protocol with user identification and transaction linking capabilities provided by the private server, strictly to solve privacy concerns.

Research paper thumbnail of Improving Internet of Things (IoT) Security with Software-Defined Networking (SDN)

Computers

There has been an increase in the usage of Internet of Things (IoT), which has recently become a ... more There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the market, security aspects are often overlooked as it takes time to investigate all the possible vulnerabilities. Since IoT devices are internet-based and include sensitive and confidential information, security concerns have been raised and several researchers are exploring methods to improve the security among these types of devices. Software defined networking (SDN) is a promising computer network technology which introduces a central program named ‘SDN Controller’ that allows overall control of the network. Hence, using SDN is an obvious solution to improve IoT networking performance and overcome shortcomings that currently exist. In thi...

Research paper thumbnail of Hierarchical Graph Transformer Based Deep Learning Model for Large-Scale Multi-Label Text Classification

IEEE Access

Traditional methods of multi-label text classification, particularly deep learning, have achieved... more Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential text information, while ignoring the logic and internal hierarchy of the text itself. Although these approaches can learn the hypothetical hierarchy and logic of the text, it is unexplained. In addition, the traditional approach treats labels as independent individuals and ignores the relationships between them, which not only does not reflect reality but also causes significant loss of semantic information. In this paper, we propose a novel Hierarchical Graph Transformer based deep learning model for large-scale multi-label text classification. We first model the text into a graph structure that can embody the different semantics of the text and the connections between them. We then use a multi-layer transformer structure with a multihead attention mechanism at the word, sentence, and graph levels to fully capture the features of the text and observe the importance of the separate parts. Finally, we use the hierarchical relationship of the labels to generate the representation of the labels, and design a weighted loss function based on the semantic distances of the labels. Extensive experiments conducted on three benchmark datasets demonstrated that the proposed model can realistically capture the hierarchy and logic of text and improve performance compared with the state-of-the-art methods.

Research paper thumbnail of Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

IEEE Access

With the rising popularity of social networks and service recommendations, research on new method... more With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical locations, and common friends), dynamic behaviors (e.g., liking, making comments, forwarding and @), and network structures (e.g., social ties) to recommend a new friend to a target user. Meanwhile, with the advent of deep learning, it has become more challenging to integrate these features into a deep neural network framework for friend recommendation. For example, how do we optimally make use of these features to form a united framework and what type of deep neural network architecture should be introduced into a novel recommendation method in an edge computing environment? In this paper, we propose DFRec++, a hybrid deep neural network framework combining attribute attention and network embeddings to make social friend recommendations with the help of both interactive semantics and contextual enhancement. More specifically, we first utilize the latent dirichlet allocation (LDA) topic model to generate common interest topics between users and compute the similarity of the explicit static attribute vector representation of topics, locations, and common friends. Then we feed dynamic behavior attributes into a convolutional neural network (CNN) to obtain the implicit vector representation of the interactions and context between two users. Subsequently, a multi-attention mechanism is designed to further improve the deep vector representation of the attribute information. Next, the LINE-based network embeddings algorithm is applied to embed the network structure into a low-dimensional vector. Finally, the attribute attention vector and the network embeddings are concatenated to form a deep feature representation, which is subsequently fed to a fully connected neural network (FCNN) to capture the probability of friendship between two users. The output of FCNN indicates the probability of two users becoming friends. We conducted experiments on a real-world Weibo dataset and the results show that DFRec++ outperforms several existing methods.

Research paper thumbnail of A Novel Security Protocol for Wireless Sensor Networks Based on Elliptic Curve Signcryption

International Journal of Computer Networks & Communications

With the growing usage of wireless sensors in a variety of applications including Internet of Thi... more With the growing usage of wireless sensors in a variety of applications including Internet of Things, the security aspects of wireless sensor networks have been on priority for the researchers. Due to the constraints of resources in wireless sensor networks, it has been always a challenge to design efficient security protocols for wireless sensor networks. An novel elliptic curve signcryption based security protocol for wireless sensor networks has been presented in this paper, which provides anonymity, confidentiality, mutual authentication, forward security, secure key establishment, and key privacy at the same time providing resistance from replay attack, impersonation attack, insider attack, offline dictionary attack, and stolen-verifier attack. Results have revealed that the proposed elliptic curve signcryption based protocol consumes the least time in comparison to other protocols while providing the highest level of security.

Research paper thumbnail of Collab-SAR: A Collaborative Avalanche Search-and-Rescue Missions Exploiting Hostile Alpine Networks

IEEE Access

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and ... more Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time-and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal. INDEX TERMS Air-ground collaborative wireless network, alpine scenarios, unmanned aerial robots, unmanned ground vehicles, WiMAX, α-level probabilistic search technique.

Research paper thumbnail of Guest Editorial Special Issue on Emerging Social Internet of Things: Recent Advances and Applications

IEEE Internet of Things Journal

Research paper thumbnail of Reliable Decision Making of Accepting Friend Request on Online Social Networks

IEEE Access

Online social network (OSN) is a platform, where users are able to share information among them e... more Online social network (OSN) is a platform, where users are able to share information among them easily and instantly. The sensitive information of an user can be misused by his/her friends or friends of friends due to the lack of reliable friend request acceptance (FRA), which is one of the key issues in OSNs. The existing FRA techniques are functioning based on either blind (i.e., without knowing information of a user, who sends the friend request to become a new friend, referred to as friend-to-be) or manual search method. Although, in the second method, the OSN user accepts a new friend based on his/her profile, however, it is not guaranteed that the profile is not fake. A approach is to bring down the misused information by filtering FRA using a reliable method to find out more information about the friend-to-be. This paper has proposed such a method for reliable decision making (RDM) of accepting friend request on OSNs in order to identify the attributes of a friend-to-be. RDM is a function with several parameters, such as security, flexibility, effectiveness, and satisfaction. To prove the reliability of the proposed method, an extensive quantitative study was carried out, which results indicated user's preferences for proposed method compared with the existing FRA methods. INDEX TERMS Friend request acceptance, online social networks (OSNs), reliable decision making.

Research paper thumbnail of Mobile Target Tracking with Multiple Objectives in Wireless Sensor Networks

Research paper thumbnail of Local Monitoring and Maintenance for Operational Wireless Sensor Networks

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013

One of the key mechanisms underlying a wireless sensor network (WSN) is to monitor the network it... more One of the key mechanisms underlying a wireless sensor network (WSN) is to monitor the network itself. Many existing approaches perform centralized analysis and maintenance based on a large amount of status reports collected from the WSN, while others use add-on protocols/modules that not only require extra management cost but also interrupt the normal operations of targeted WSN applications. Unlike existing work, we propose LoMoM, a new approach of Local Monitoring and Maintenance for a WSN, which combines monitoring operations for the WSN with the operations of a mobile event monitoring, in a manner that is both energy-and latency-efficient. LoMoM includes a two-part monitoring architecture: a WSN and a 3G network. Our main interest is in the WSN-part, where we address two important issues: monitoring probable anomalies/faults of the nodes, and the link failures. To achieve the event monitoring efficiently, LoMoM conducts a prompt local maintenance when such a fault occurs. Fault and event detection status reports are observed by a remote monitoring center using the 3G network. Comprehensive evaluations via both simulations and real-world experiments are conducted to validate the effectiveness of LoMoM.

Research paper thumbnail of Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

Future Generation Computer Systems, 2013

h i g h l i g h t s • We present an auction mechanism for the problem of target tracking in WSNs.... more h i g h l i g h t s • We present an auction mechanism for the problem of target tracking in WSNs. • We propose an adaptive sensor activation algorithm for the target tracking. • We improve prior trilateration algorithm for low target localization errors. • The proposed algorithms provide high energy-efficiency and tracking quality.

Research paper thumbnail of Impact of Pursuer Speed on the Performance of Distributed Surveillance Sensor Network

Abstract The main purpose of developing a surveillance system is to become aware of unauthorized ... more Abstract The main purpose of developing a surveillance system is to become aware of unauthorized target in wireless sensor networks (WSNs). The challenge is how efficiently and fast the traversal can be perceived in a WSN. This paper demonstrates the ...

Research paper thumbnail of Backup Sensor Placement with Guaranteed Fault Tolerance for Structural Health Monitoring

Wireless Sensor Networks (WSNs) are being increasingly deployed for data-intensive structural hea... more Wireless Sensor Networks (WSNs) are being increasingly deployed for data-intensive structural health monitoring (SHM). The most critical problem for a structural health monitoring (SHM) system is sensor placement. In this work, we propose to deploy a set of backup sensors for SHM by finding locations around the possible network failure points, also known as critical points. The processes of finding critical points and locations for the backup sensors are performed through decentralized clusters. We found that the sensors in the clusters are weakly connected because civil engineering does not consider connectivity between sensors but ensures the quality of the sensor placement. The objective of our backup sensor placement (BSP) is to guarantee that the network will remain connected in the event of a single or multiple sensors failure during the SHM operation and thus prolong the network lifetime under the connectivity and data delivery constraints. We extensively evaluated our approach under a real system implementation and discussed its performance in the context of both WSN and SHM requirements. Experimental results indicated that the fault tolerance can effectively prolong the network lifetime.

Research paper thumbnail of Sensor Placement with Multiple Objectives for Structural Health Monitoring in WSNs

Structural health monitoring (SHM) refers to the process of implementing a damage detection and c... more Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop "connectivity trees" in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

Research paper thumbnail of An algorithm for determining neural network architecture using differential evolution

Page 1. An Algorithm for Determining Neural Network Architecture Using Differential Evolution Md.... more Page 1. An Algorithm for Determining Neural Network Architecture Using Differential Evolution Md. Zakirul Alam Bhuiyan School of Information Science and Engineering Central South University Changsha, China e-mail: zakirul_alam@yahoo.com ...

Research paper thumbnail of Prediction-based energy-efficient target tracking protocol in wireless sensor networks

A prediction based energy-efficient target tracking protocol in wireless sensor networks (PET) wa... more A prediction based energy-efficient target tracking protocol in wireless sensor networks (PET) was proposed for tracking a mobile target in terms of sensing and communication energy consumption. In order to maximize the lifetime of a wireless sensor network (WSN), the volume of messages and the time for neighbor discovery operations were minimized. The target was followed in a special region known as a face obtained by planarization technique in face-aware routing. An election process was conducted to choose a minimal number of appropriate sensors that are the nearest to the target and a wakeup strategy was proposed to wakeup the appropriate sensors in advance to track the target. In addition, a tracking algorithm to track a target step by step was introduced. Performance analysis and simulation results show that the proposed protocol efficiently tracks a target in WSNs and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

Research paper thumbnail of Target tracking with monitor and backup sensors in wireless sensor networks

We propose target tracking with monitor and backup sensors in wireless sensor networks (TTMB) to ... more We propose target tracking with monitor and backup sensors in wireless sensor networks (TTMB) to increase the energy efficiency of the network and decrease the target capturing time while considering the effect of a target's variable velocity and direction. The approach is based on a face routing and prediction method. We use a state transition strategy, a dynamic energy consumption model, and a moving target positioning model to reduce energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and sensing for target tracking. Two sensor nodes, namely, 'Monitor' and 'Backup', are employed for target tracking for each period of time. For the whole time of target tracking, a linked list of monitor and backup sensors is formed. If either monitor or backup sensor fails, this approach can still survive. Simulation results compared with existing protocols show better tracking accuracy, faster target capturing speed, and better energy efficiency.

Research paper thumbnail of Two-level cooperative and energy-efficient tracking algorithm in wireless sensor networks

Concurrency and Computation: Practice and Experience, 2010

Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and... more Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and locating a target in a large deployment area but most systems require high transmission power levels and a large volume of messages, much time for neighbor discovery operations, and many sensors to detect the target at a given time. We propose a two-level cooperative and energy-efficient tracking algorithm (CET) that reduces energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and perform sensing for target tracking in wireless sensor networks. It is expected that only the nodes adjacent to the target are responsible for observing the target to save the energy consumption and extend the network lifetime as well by using a wakeup mechanism and a face-aware routing. Through performance analysis and simulation studies, we demonstrate that CET improves target capturing speed and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

Research paper thumbnail of Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events

Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical c... more Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed "event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)" by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution to adaptive sampling that automatically switches between high-and low-frequency intervals to reduce the resource usage while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained WSNs. In the absence of an event, "uninteresting" data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.

Research paper thumbnail of Security and Attack Vector Analysis of IoT Devices

Security, Privacy, and Anonymity in Computation, Communication, and Storage

Research paper thumbnail of An Anonymous Protocol with User Identification and Linking Capabilities for User Privacy in a Permissioned Blockchain

Electronics

A permissioned blockchain includes a user in the network after verifying the user’s identity, in ... more A permissioned blockchain includes a user in the network after verifying the user’s identity, in contrast to Bitcoin, which is a public blockchain that allows network participation without third-party approval. The two types of permissioned blockchains are private blockchains, each consisting of one server and multiple users, and consortium blockchains, which consist of groups of private blockchains. However, a blockchain has privacy issues, such as user tracking and inference. Therefore, cryptography should be applied for user privacy in a blockchain. There is a lot of research on anonymous protocols for privacy in a blockchain. In this paper, we provide a scheme for user management, i.e., identification and authorization, in a permissioned blockchain. We also propose an anonymous protocol with user identification and transaction linking capabilities provided by the private server, strictly to solve privacy concerns.

Research paper thumbnail of Improving Internet of Things (IoT) Security with Software-Defined Networking (SDN)

Computers

There has been an increase in the usage of Internet of Things (IoT), which has recently become a ... more There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the market, security aspects are often overlooked as it takes time to investigate all the possible vulnerabilities. Since IoT devices are internet-based and include sensitive and confidential information, security concerns have been raised and several researchers are exploring methods to improve the security among these types of devices. Software defined networking (SDN) is a promising computer network technology which introduces a central program named ‘SDN Controller’ that allows overall control of the network. Hence, using SDN is an obvious solution to improve IoT networking performance and overcome shortcomings that currently exist. In thi...

Research paper thumbnail of Hierarchical Graph Transformer Based Deep Learning Model for Large-Scale Multi-Label Text Classification

IEEE Access

Traditional methods of multi-label text classification, particularly deep learning, have achieved... more Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential text information, while ignoring the logic and internal hierarchy of the text itself. Although these approaches can learn the hypothetical hierarchy and logic of the text, it is unexplained. In addition, the traditional approach treats labels as independent individuals and ignores the relationships between them, which not only does not reflect reality but also causes significant loss of semantic information. In this paper, we propose a novel Hierarchical Graph Transformer based deep learning model for large-scale multi-label text classification. We first model the text into a graph structure that can embody the different semantics of the text and the connections between them. We then use a multi-layer transformer structure with a multihead attention mechanism at the word, sentence, and graph levels to fully capture the features of the text and observe the importance of the separate parts. Finally, we use the hierarchical relationship of the labels to generate the representation of the labels, and design a weighted loss function based on the semantic distances of the labels. Extensive experiments conducted on three benchmark datasets demonstrated that the proposed model can realistically capture the hierarchy and logic of text and improve performance compared with the state-of-the-art methods.

Research paper thumbnail of Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

IEEE Access

With the rising popularity of social networks and service recommendations, research on new method... more With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical locations, and common friends), dynamic behaviors (e.g., liking, making comments, forwarding and @), and network structures (e.g., social ties) to recommend a new friend to a target user. Meanwhile, with the advent of deep learning, it has become more challenging to integrate these features into a deep neural network framework for friend recommendation. For example, how do we optimally make use of these features to form a united framework and what type of deep neural network architecture should be introduced into a novel recommendation method in an edge computing environment? In this paper, we propose DFRec++, a hybrid deep neural network framework combining attribute attention and network embeddings to make social friend recommendations with the help of both interactive semantics and contextual enhancement. More specifically, we first utilize the latent dirichlet allocation (LDA) topic model to generate common interest topics between users and compute the similarity of the explicit static attribute vector representation of topics, locations, and common friends. Then we feed dynamic behavior attributes into a convolutional neural network (CNN) to obtain the implicit vector representation of the interactions and context between two users. Subsequently, a multi-attention mechanism is designed to further improve the deep vector representation of the attribute information. Next, the LINE-based network embeddings algorithm is applied to embed the network structure into a low-dimensional vector. Finally, the attribute attention vector and the network embeddings are concatenated to form a deep feature representation, which is subsequently fed to a fully connected neural network (FCNN) to capture the probability of friendship between two users. The output of FCNN indicates the probability of two users becoming friends. We conducted experiments on a real-world Weibo dataset and the results show that DFRec++ outperforms several existing methods.

Research paper thumbnail of A Novel Security Protocol for Wireless Sensor Networks Based on Elliptic Curve Signcryption

International Journal of Computer Networks & Communications

With the growing usage of wireless sensors in a variety of applications including Internet of Thi... more With the growing usage of wireless sensors in a variety of applications including Internet of Things, the security aspects of wireless sensor networks have been on priority for the researchers. Due to the constraints of resources in wireless sensor networks, it has been always a challenge to design efficient security protocols for wireless sensor networks. An novel elliptic curve signcryption based security protocol for wireless sensor networks has been presented in this paper, which provides anonymity, confidentiality, mutual authentication, forward security, secure key establishment, and key privacy at the same time providing resistance from replay attack, impersonation attack, insider attack, offline dictionary attack, and stolen-verifier attack. Results have revealed that the proposed elliptic curve signcryption based protocol consumes the least time in comparison to other protocols while providing the highest level of security.

Research paper thumbnail of Collab-SAR: A Collaborative Avalanche Search-and-Rescue Missions Exploiting Hostile Alpine Networks

IEEE Access

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and ... more Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time-and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal. INDEX TERMS Air-ground collaborative wireless network, alpine scenarios, unmanned aerial robots, unmanned ground vehicles, WiMAX, α-level probabilistic search technique.

Research paper thumbnail of Guest Editorial Special Issue on Emerging Social Internet of Things: Recent Advances and Applications

IEEE Internet of Things Journal

Research paper thumbnail of Reliable Decision Making of Accepting Friend Request on Online Social Networks

IEEE Access

Online social network (OSN) is a platform, where users are able to share information among them e... more Online social network (OSN) is a platform, where users are able to share information among them easily and instantly. The sensitive information of an user can be misused by his/her friends or friends of friends due to the lack of reliable friend request acceptance (FRA), which is one of the key issues in OSNs. The existing FRA techniques are functioning based on either blind (i.e., without knowing information of a user, who sends the friend request to become a new friend, referred to as friend-to-be) or manual search method. Although, in the second method, the OSN user accepts a new friend based on his/her profile, however, it is not guaranteed that the profile is not fake. A approach is to bring down the misused information by filtering FRA using a reliable method to find out more information about the friend-to-be. This paper has proposed such a method for reliable decision making (RDM) of accepting friend request on OSNs in order to identify the attributes of a friend-to-be. RDM is a function with several parameters, such as security, flexibility, effectiveness, and satisfaction. To prove the reliability of the proposed method, an extensive quantitative study was carried out, which results indicated user's preferences for proposed method compared with the existing FRA methods. INDEX TERMS Friend request acceptance, online social networks (OSNs), reliable decision making.

Research paper thumbnail of Mobile Target Tracking with Multiple Objectives in Wireless Sensor Networks

Research paper thumbnail of Local Monitoring and Maintenance for Operational Wireless Sensor Networks

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013

One of the key mechanisms underlying a wireless sensor network (WSN) is to monitor the network it... more One of the key mechanisms underlying a wireless sensor network (WSN) is to monitor the network itself. Many existing approaches perform centralized analysis and maintenance based on a large amount of status reports collected from the WSN, while others use add-on protocols/modules that not only require extra management cost but also interrupt the normal operations of targeted WSN applications. Unlike existing work, we propose LoMoM, a new approach of Local Monitoring and Maintenance for a WSN, which combines monitoring operations for the WSN with the operations of a mobile event monitoring, in a manner that is both energy-and latency-efficient. LoMoM includes a two-part monitoring architecture: a WSN and a 3G network. Our main interest is in the WSN-part, where we address two important issues: monitoring probable anomalies/faults of the nodes, and the link failures. To achieve the event monitoring efficiently, LoMoM conducts a prompt local maintenance when such a fault occurs. Fault and event detection status reports are observed by a remote monitoring center using the 3G network. Comprehensive evaluations via both simulations and real-world experiments are conducted to validate the effectiveness of LoMoM.

Research paper thumbnail of Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

Future Generation Computer Systems, 2013

h i g h l i g h t s • We present an auction mechanism for the problem of target tracking in WSNs.... more h i g h l i g h t s • We present an auction mechanism for the problem of target tracking in WSNs. • We propose an adaptive sensor activation algorithm for the target tracking. • We improve prior trilateration algorithm for low target localization errors. • The proposed algorithms provide high energy-efficiency and tracking quality.

Research paper thumbnail of Impact of Pursuer Speed on the Performance of Distributed Surveillance Sensor Network

Abstract The main purpose of developing a surveillance system is to become aware of unauthorized ... more Abstract The main purpose of developing a surveillance system is to become aware of unauthorized target in wireless sensor networks (WSNs). The challenge is how efficiently and fast the traversal can be perceived in a WSN. This paper demonstrates the ...

Research paper thumbnail of Backup Sensor Placement with Guaranteed Fault Tolerance for Structural Health Monitoring

Wireless Sensor Networks (WSNs) are being increasingly deployed for data-intensive structural hea... more Wireless Sensor Networks (WSNs) are being increasingly deployed for data-intensive structural health monitoring (SHM). The most critical problem for a structural health monitoring (SHM) system is sensor placement. In this work, we propose to deploy a set of backup sensors for SHM by finding locations around the possible network failure points, also known as critical points. The processes of finding critical points and locations for the backup sensors are performed through decentralized clusters. We found that the sensors in the clusters are weakly connected because civil engineering does not consider connectivity between sensors but ensures the quality of the sensor placement. The objective of our backup sensor placement (BSP) is to guarantee that the network will remain connected in the event of a single or multiple sensors failure during the SHM operation and thus prolong the network lifetime under the connectivity and data delivery constraints. We extensively evaluated our approach under a real system implementation and discussed its performance in the context of both WSN and SHM requirements. Experimental results indicated that the fault tolerance can effectively prolong the network lifetime.

Research paper thumbnail of Sensor Placement with Multiple Objectives for Structural Health Monitoring in WSNs

Structural health monitoring (SHM) refers to the process of implementing a damage detection and c... more Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop "connectivity trees" in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

Research paper thumbnail of An algorithm for determining neural network architecture using differential evolution

Page 1. An Algorithm for Determining Neural Network Architecture Using Differential Evolution Md.... more Page 1. An Algorithm for Determining Neural Network Architecture Using Differential Evolution Md. Zakirul Alam Bhuiyan School of Information Science and Engineering Central South University Changsha, China e-mail: zakirul_alam@yahoo.com ...

Research paper thumbnail of Prediction-based energy-efficient target tracking protocol in wireless sensor networks

A prediction based energy-efficient target tracking protocol in wireless sensor networks (PET) wa... more A prediction based energy-efficient target tracking protocol in wireless sensor networks (PET) was proposed for tracking a mobile target in terms of sensing and communication energy consumption. In order to maximize the lifetime of a wireless sensor network (WSN), the volume of messages and the time for neighbor discovery operations were minimized. The target was followed in a special region known as a face obtained by planarization technique in face-aware routing. An election process was conducted to choose a minimal number of appropriate sensors that are the nearest to the target and a wakeup strategy was proposed to wakeup the appropriate sensors in advance to track the target. In addition, a tracking algorithm to track a target step by step was introduced. Performance analysis and simulation results show that the proposed protocol efficiently tracks a target in WSNs and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

Research paper thumbnail of Target tracking with monitor and backup sensors in wireless sensor networks

We propose target tracking with monitor and backup sensors in wireless sensor networks (TTMB) to ... more We propose target tracking with monitor and backup sensors in wireless sensor networks (TTMB) to increase the energy efficiency of the network and decrease the target capturing time while considering the effect of a target's variable velocity and direction. The approach is based on a face routing and prediction method. We use a state transition strategy, a dynamic energy consumption model, and a moving target positioning model to reduce energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and sensing for target tracking. Two sensor nodes, namely, 'Monitor' and 'Backup', are employed for target tracking for each period of time. For the whole time of target tracking, a linked list of monitor and backup sensors is formed. If either monitor or backup sensor fails, this approach can still survive. Simulation results compared with existing protocols show better tracking accuracy, faster target capturing speed, and better energy efficiency.

Research paper thumbnail of Two-level cooperative and energy-efficient tracking algorithm in wireless sensor networks

Concurrency and Computation: Practice and Experience, 2010

Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and... more Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and locating a target in a large deployment area but most systems require high transmission power levels and a large volume of messages, much time for neighbor discovery operations, and many sensors to detect the target at a given time. We propose a two-level cooperative and energy-efficient tracking algorithm (CET) that reduces energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and perform sensing for target tracking in wireless sensor networks. It is expected that only the nodes adjacent to the target are responsible for observing the target to save the energy consumption and extend the network lifetime as well by using a wakeup mechanism and a face-aware routing. Through performance analysis and simulation studies, we demonstrate that CET improves target capturing speed and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

Research paper thumbnail of Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events

Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical c... more Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed "event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)" by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution to adaptive sampling that automatically switches between high-and low-frequency intervals to reduce the resource usage while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained WSNs. In the absence of an event, "uninteresting" data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.