Ensuring the security and privacy of RFID systems is paramount to prevent unauthorized access and data breaches. In addition, addressing the insecurity of public keys in RFID tags and preventing server-side tag distinction requires a multifaceted approach. This paper proposes an RFID-enabled protocol, calledRISE, Resource-Efficient RFID Security Protocol inIoT withS-Box andElliptic Curve Cryptography, that integrates S-box and elliptic curve cryptography (ECC) to enhance the security of RFID communication. The protocol leverages S-boxes to introduce non-linearity and confusion, thereby thwarting potential attacks such as cryptanalysis and replay attacks. Additionally, ECC is employed for key exchange and authentication, leveraging computational efficiency and strong security properties. Formal proofs and analysis results demonstrate thatRISEprovides robust security and can effectively resist various types of attacks.RISEpresents notable improvements, with an average 51.18% reduction in computational overhead compared to similar protocols. This advancement not only boosts performance but also enhances resource efficiency.

The Internet-of-Things (IoT) enables the connection of myriad wireless devices, generating a massive influx of data that can overwhelm central servers. Federated learning (FL) mitigates these issues by distributing computation tasks across edge devices, thus preserving data privacy by eliminating the need for raw data transmission. However, when deployed in large-scale IoT networks, FL encounters several challenges such as device energy constraints, heterogeneous network conditions, and the straggler effect, which inevitably impedes model convergence. In response, this paper proposes a semi-decentralized hybrid FL (SDHFL) scheme leveraging an unmanned aerial vehicle (UAV) as a mobile data center to collect data from distributed IoT clusters. To further expedite FL convergence, we formulate a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize overall completion time while ensuring quality of service (QoS) requirements, considering both the UAV's energy constraints and network stability. We demonstrate that efficient learning is achieved by optimizing both power allocation and device computational capability. Furthermore, we prove the convergence of the proposed SDHFL scheme and derive the minimum number of global iterations required. Exploiting these insights, we introduce a low-complexity suboptimal algorithm for dynamic cluster selection and resource allocation optimization, leveraging Lyapunov optimization theory to obtain optimal solutions efficiently, demonstrating its scalability for large-scale networks. Our simulation results validate the effectiveness of the proposed SDHFL framework, revealing a non-trivial tradeoff where the overall completion time initially decreases and then increases as the number of devices or clusters grows, indicating the necessity of optimizing both cluster counts and intra-cluster device allocations. Furthermore, compared to several baseline schemes, our proposed optimal algorithms significantly reduce overall completion time and enhance model convergence, demonstrating the potential of SDHFL for enabling efficient and scalable FL in IoT networks.

The combination of reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) can improve the resource utilization in non-line-of-sight scenarios. To sense the target with high accuracy, it is necessary to directionally reflect the sensing signal toward the sensing target via the RIS, improving the sensing performance. However, enhancing signal quality may increase the risk of information leakage when the sensing target is the warden. Against this background, we investigate a covert transmission problem in an RIS assisted ISAC system. Specifically, we obtain a tractable form of covertness constraint in terms of minimum detection error probability via the optimal detection threshold. Then, we maximize the sum covert transmission rate by jointly optimizing the beamforming of confidential signal and jamming signal as well as the RIS’s phase shift, while ensuring the reliability, covertness and sensing constraints. Owing to the effectiveness of the deep reinforcement learning algorithm in processing high-dimensional data and making intelligent decision, we propose a twins-deep deterministic policy gradient-based joint covert beamforming and the phase shift of RIS optimization (TD3-CBP) algorithm to solve the above non-convex problem. Finally, simulation results demonstrate the effectiveness of the proposed TD3-CBP algorithm in the covertness performance, achieving an average of 16.1 % higher sum covert transmission rate than the benchmark algorithms.

In developing Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traffic sign detection is a critical task. However, existing methods face limitations in real-time performance and small-object detection accuracy, partly due to deployment platform constraints. Our proposed Neck-Heavy YOLOv5 (NH-YOLOv5) introduces significant improvements. It features a hybrid data augmentation approach that enhances the detection of small objects. Moreover, a unique neck-heavy network architecture is designed to optimize detection performance without increasing computational complexity. Notably, NH-YOLOv5 is integrated into the digital twin framework of traffic systems. By leveraging the digital twin’s ability to simulate and monitor traffic scenarios in real time, the model can continuously adapt and improve its detection capabilities. Experimental results based on the Tsinghua - Tencent 100K (TT100K) dataset demonstrate that our method achieves a 53% reduction in computation while enhancing accuracy and small object detection ability, making it highly suitable for real-time traffic sign detection in the context of digital twins.

The combination of reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) can improve the resource utilization in non-line-of-sight scenarios. However, the private information in this situation raises security concerns when the transmission behavior is detected by wardens. Against this background, we investigate a covert transmission problem in an RIS assisted ISAC system. Specifically, we obtain a tractable form of covertness constraint in terms of minimum detection error probability via the optimal detection threshold, paving the way for optimization process. Then, the sum covert transmission rate is maximized by jointly optimizing the beamforming of confidential signal and jamming signal as well as the RIS’s phase shift. To solve the above non-convex problem, we propose a joint covert beamforming and the phase shift of RIS optimization-based twins-deep deterministic policy gradient (CBP-TD3) algorithm. Finally, simulation results demonstrate the effectiveness of the proposed CBP-TD3 algorithm in the covertness.

Automatic modulation classification (AMC) is a fundamental technology for identifying modulation types in non-cooperative communication systems. It plays a crucial role in various applications, including spectrum monitoring, cognitive radio, and signal intelligence. Recently, deep learning (DL) based AMC methods have achieved remarkable classification accuracy. However, their practical deployment in resource-constrained edge devices remains challenging due to their high computational complexity and excessive model size. To address this limitation, we propose an ultra-lightweight AMC method based on filter pruning via geometric median (FPGM). The key idea is to leverage the geometric median as a robustness-driven filter selection criterion, effectively eliminating redundant convolutional kernels while preserving essential model representations. Specifically, we first determine the geometric median of the filters in each layer, which effectively represents the distribution of filters within that layer. Then, filters near the geometric median are identified and filtered out through the characteristics of the geometric median. Finally, the performance degradation of the model caused by the removal of filters can be restored through fine-tuning. Experimental results demonstrate that the proposed AMC method achieves a 99% reduction in model size while limiting the classification accuracy drop to merely 1.61%, significantly outperforming other lightweight AMC techniques. These results highlight the feasibility of deploying the proposed AMC model on edge Internet of Things (IoT) devices, enabling efficient real-time modulation classification with minimal computational overhead.

Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To address this limitation, some studies have integrated graph attention mechanisms to capture the influence of neighboring vehicles during the aggregation process. However, Graph Attention Networks (GATs) are constrained by the univariate nature of attention heads and coefficients, thus lacking flexibility. In this work, we not only consider the social dynamics among neighboring vehicles but also delve into the limitations of GAT models. We propose a Vehicular Social Dynamics Anomaly Detection (VSD-AD) model based on the Recurrent Multi-Mask Aggregator (MMA) enabled Variational AutoEncoder (VAE) architecture to maximize the learning of relational embeddings among neighbors in a highway vehicle network. Furthermore, we apply Node Feature Quantisation (NFQ) to the encoder output to mitigate the complexity of neighbor relationships. Our model is flexible and customizable for different highway scenarios, suitable for large-scale highway vehicle video data. To validate real-world applicability, we further assess its performance on both the simulated dataset and real-world traffic dataset, where our model outperforms other mainstream methods in terms of detection performance.

This faculty leadership panel will be held jointly by the IEEE Education Society (IEEE EdSoc) and IEEE Educational Activities Board (EAB) Faculty Resources Committee (FRC). The panel intends to provide participants introductory knowledge and skills. Two moderators and four panelists will focus on discussing their various academic leadership experiences and practices. The panel will be held in an interactive way so that the participants will have enough time to ask questions or communicate with the panelists. It is expected that this joint panel may potentially help to build up networks and mentorships.

Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (PBSCF-ITS). The proposed PBSCF-ITS guarantees access control and key management among the vehicle to vehicle, vehicle to roadside unit, and roadside unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network-related performance parameters, like the number of mined blocks and transactions per block.

Inappropriate service migrations can lead to undesirable situations, such as high traffic overhead, long service latency, and service disruption. In this article, we propose an application-aware migration algorithm (AMA) with prefetching. In AMA, a mobile device sends a service offloading request to the controller. After receiving this request, the controller determines the initial service cloud where virtual machine (VM) of the service initially operates by considering the application type. In addition, it periodically decides where to migrate VM and prefetch its core part considering the mobility of the mobile device and application type. To minimize the generated traffic volume while satisfying the requirements of the application, a constrained Markov decision process (CMDP) is formulated and its optimal policy is obtained via linear programming. Evaluation results demonstrate that AMA with the optimal policy can reduce the generated traffic volume while satisfying the requirements of the application (i.e., service latency and probability of service disruption).

A blockchain-based smart farming technology provides the agricultural data to the farmers and other users associated with smart farming on a single integrated platform. Moreover, persistence and auditability of stored data in blocks into the blockchain provide the confidence of using the correct data when needed later and adds transparency, anonymity, and traceability at the same time. To fulfill such a goal, in this article, we design a new smart contract-based blockchain-envisioned authenticated key agreement mechanism in a smart farming environment. The device-to-device (D2D) authentication phase and device-to-gateway (D2G) authentication phase support mutual authentication and key agreement between two Internet-of-Things (IoT)-enabled devices and between an IoT device and the gateway node (GWN) in the network, respectively. The blocks are created by the edge servers on the authenticated data of IoT devices received from the GWNs and then sent to the cloud server (CS). The smart contract-based consensus mechanism allows verification and addition of the formed blocks by a peer-to-peer (P2P) CSs network. The security of the proposed scheme is done through formal and informal security analysis, and also using the formal security verification tool. A detailed comparative study reveals that the proposed scheme offers superior security and more functionality features as compared to existing competing authentication protocols. Finally, the blockchain-based simulation has been conducted to measure computational time for a varied number of mined blocks and also a varied number of transactions per block.

Recent technological evolution in the Internet of Things (IoT) age supports better solutions to magnify the management of the power quality and reliability concerns, and imposes the measures of a smart grid. In smart grid environment, a smart meter needs to securely access the services from a service provider via insecure channel. However, since the communication is via public channel, it imposes various security threats by an adversary. To deal with this, in this article we design a new anonymous signature-based authenticated key exchange scheme for IoT-enabled smart grid environment, called AAS-IoTSG. The dynamic smart meter addition phase is also permissible in AAS-IoTSG after initial deployment. The security of AAS-IoTSG has been tested rigorously using formal security analysis under the real-or-random (ROR) model which is one of the broadly-accepted standard random oracle models, formal security verification under the broadly-used automated validation of Internet security protocols and applications (AVISPA) tool and also using informal security analysis. Finally, an exhaustive comparative study unveils that AAS-IoTSG supports better security and functionality features and requires less communication and computation overheads as compared to the existing state-of-art authentication mechanisms in smart grid systems.

Because the number of Internet of Things (IoT) devices has increased dramatically in both licensed and unlicensed band networks, wireless networks on licensed bands for the IoT, such as Long Term Evolution (LTE), are expected to become overloaded. Wireless energy harvesting in IoT networks also needs to be offloaded from existing licensed bands like LTE and onto unlicensed bands, such as wireless fidelity (WiFi). This paper proposes offloading channel resources from licensed bands to unlicensed bands for wireless IoT-device energy harvesting. Our proposed scheme is based on the licensed-assisted access protocol for transferring power packets as well as information. We propose a queueing model for energy storage in IoT devices and consumption behavior in batteries, and we propose a probability model of empty energy. Simulation proves that our proposed offloading scheme works properly for harvesting wireless energy through a 5-GHz unlicensed channel without affecting the performance of WiFi stations for IoT devices.

As the “Internet communications infrastructure” develops to encircle smart devices, it is very much essential for designing suitable methods for secure communications with these smart devices, in the future Internet of Things (IoT) applications context. Due to wireless communication among the IoT smart devices and the gateway node (GWN), several security threats may arise in the IoT environment, including replay, man-in-the-middle, impersonation, malicious devices deployment, and physical devices capture attacks. In this article, to mitigate such security threats, we design a new certificate-based device access control scheme in IoT environment which is not only secure against mentioned attacks, but it also preserves anonymity property. A detailed security analysis using the widely accepted real-or-random (ROR) model-based formal security analysis, informal security analysis, and also formal security verification based on the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool has been performed on the proposed scheme to show that it is secure against various known attacks. In addition, a comprehensive comparative analysis among the proposed scheme and other relevant schemes shows that a better tradeoff among the security and functionality attributes, communication, and computational costs is achieved for the proposed scheme as compared to other schemes.

Mobile edge computing (MEC) is used to offload services (tasks) from cloud computing in order to deliver those services to mobile Internet of Things (IoT) devices near mobile edge nodes. However, even though there are advantages to MEC, we face many significant problems, such as how a service provider (SP) deploys requested services efficiently on a destination MEC node, and how to discover existing services in neighboring MEC nodes to save edge resources. In this paper, we present a decentralized and revised content-centric networking (CCN)-based MEC service deployment/discovery protocol and platform. We organized a gateway in every area according to a three-tiered hierarchical MEC network topology to reduce computing overhead at the centralized controller. We revised CCN to introduce a protocol to help SP deploy their service on MEC node and assist MEC node discover services in neighboring nodes. By using our proposed protocol, MEC nodes can deploy or discover the requested service instances in the proximity of IoT devices to reduce transmission delay. We also present a mathematical model to calculate the round trip time to guarantee quality of service. Numerical experiments measure the performance of our proposed method with various mobile IoT device services. The results show that the proposed service deployment protocol and platform reduce the average service delay by up to 50% compared to legacy cloud. In addition, the proposed method outperforms the legacy protocol of the MEC environment in service discovery time.

Reliable data delivery in the Internet of Things (IoT) is very important in order to provide IoT-based services with the required quality. However, IoT data delivery may not be successful for different reasons, such as connection errors, external attacks, or sensing errors. This results in data incompleteness, which decreases the performance of IoT applications. In particular, the recovery of missing data among the massive sensed data of the IoT is so important that it should be solved. In this paper, we propose a probabilistic method to recover missing (incomplete) data from IoT sensors by utilizing data from related sensors. The main idea of the proposed method is to perform probabilistic matrix factorization (PMF) within the preliminary assigned group of sensors. Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm. Simulation results show that the proposed PMF model with clustering outperforms support vector machine (SVM) and deep neural network (DNN) algorithms in terms of accuracy and root mean square error. By using normalized datasets, PMF shows faster execution time than SVM, and almost the same execution time as the DNN method. This proposed incomplete data-recovery approach is a promising alternative to traditional DNN and SVM methods for IoT telemetry applications.

The recent proliferation of mobile devices, such as smartphones and wearable devices has given rise to crowdsourcing Internet of Things (IoT) applications. E-healthcare service is one of the important services for the crowdsourcing IoT applications that facilitates remote access or storage of medical server data to the authorized users (for example, doctors, patients, and nurses) via wireless communication. As wireless communication is susceptible to various kinds of threats and attacks, remote user authentication is highly essential for a hazard-free use of these services. In this paper, we aim to propose a new secure three-factor user remote user authentication protocol based on the extended chaotic maps. The three factors involved in the proposed scheme are: 1) smart card; 2) password; and 3) personal biometrics. As the proposed scheme avoids computationally expensive elliptic curve point multiplication or modular exponentiation operation, it is lightweight and efficient. The formal security verification using the widely-accepted verification tool, called the ProVerif 1.93, shows that the presented scheme is secure. In addition, we present the formal security analysis using the both widely accepted real-or-random model and Burrows-Abadi-Needham logic. With the combination of high security and appreciably low communication and computational overheads, our scheme is very much practical for battery limited devices for the healthcare applications as compared to other existing related schemes.

In recent years, the research in generic Internet of Things (IoT) attracts a lot of practical applications including smart home, smart city, smart grid, industrial Internet, connected healthcare, smart retail, smart supply chain and smart farming. The hierarchical IoT network (HIoTN) is a special kind of the generic IoT network, which is composed of the different nodes, such as the gateway node, cluster head nodes, and sensing nodes organized in a hierarchy. In HIoTN, there is a need, where a user can directly access the real-time data from the sensing nodes for a particular application in generic IoT networking environment. This paper emphasizes on the design of a new secure lightweight three-factor remote user authentication scheme for HIoTNs, called the user authenticated key management protocol (UAKMP). The three factors used in UAKMP are the user smart card, password, and personal biometrics. The security of the scheme is thoroughly analyzed under the formal security in the widely accepted real-or-random model, the informal security as well as the formal security verification using the widely accepted automated validation of Internet security protocols and applications tool. UAKMP offers several functionality features including offline sensing node registration, freely password and biometric update facility, user anonymity, and sensing node anonymity compared to other related existing schemes. In addition, UAKMP is also comparable in computation and communication costs as compared to other existing schemes.

Because of a scarcity of bandwidth due to explosive growth of mobile devices in 5G, offloading computing workload to WiFi devices can be a feasible solution. In addition, for reasons of battery capacity limitation of mobile device, wireless energy harvesting technologies play an important role in case the battery runs out in the middle of offloading processes. Thus, in this paper, to reduce both the problems, we propose the selective offloading approach for 5G mobile users to reduce execution time of a task considering proper wireless energy harvesting technology. The experimental results have shown the superiority of the proposed approach.

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. Applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

The papers in this special section examine recent advancements in the area of network big data analysis.

This paper evaluates the energy efficiency of uplink transmission in heterogeneous cellular networks (HetNets), where fractional power control (FPC) is applied at user equipments (TIEs) subject to a maximum transmit power constraint. We first consider an arbitrary deterministic HetNet and characterize the properties of energy efficiency for TIEs in different path loss regimes, or different access regions. By introducing the notion of transfer path loss, we reveal that, for TIE whose path loss is below the transfer path loss, its energy efficiency highly depends on the value of power control coefficient adopted by FPC. In contrast, for TIE with path loss above the transfer path loss, the uplink energy efficiency asymptotically decreases inversely with path loss, independent of the adopted power control coefficient. Based on these properties, we characterize the optimal power control coefficients for maximizing the energy efficiency of FPC in different access regions. Next, we extend the analysis to stochastic HetNets where TIEs and BSs are distributed as independent Poisson point processes, and investigate the distribution of transmit power for uplink TIEs. Moreover, the probability of truncation outage due to constrained maximal transmit power, as well as the average energy efficiency of TIEs are analytically derived as functions of the BS and TIE densities, power control coefficient, and receiver threshold. Simulation results validate the analytical results, show the consistency between deterministic and stochastic analyses, and suggest suitable power control coefficient for achieving energy efficient uplink transmission by FPC in HetNets.

Massive multiple‐input multiple‐output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time–frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short‐range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple‐input multiple‐output, with a focus on the challenges and opportunities, based on contemporary research.

In practical mobile communication engineering applications, surfaces of antenna array deployment regions are usually uneven. Therefore, massive multi-input-multi-output (MIMO) communication systems usually transmit wireless signals by irregular antenna arrays. To evaluate the performance of irregular antenna arrays, the matrix correlation coefficient and the ergodic received gain are defined for massive MIMO communication systems with mutual coupling effects. Furthermore, the lower bound of the ergodic achievable rate, symbol error rate, and average outage probability is first derived for multi-user massive MIMO communication systems using irregular antenna arrays. Asymptotic results are also derived when the number of antennae approaches infinity. Numerical results indicate that there exists a maximum achievable rate when the number of antennae keeps increasing in massive MIMO communication systems using irregular antenna arrays. Moreover, the irregular antenna array outperforms the regular antenna array in the achievable rate of massive MIMO communication systems when the number of antennae is larger than or equal to a given threshold.

Abstract Resource virtualization has become one of the key super‐power mobile computing architecture technologies. As mobile devices and multimedia traffic have increased dramatically, the load on mobile cloud computing systems has become heavier. Under such conditions, mobile cloud system reliability becomes a challenging task. In this paper, we propose a new model using a naive Bayes classifier for hypervisor failure prediction and prevention in mobile cloud computing. We exploit real‐time monitoring data in combination with historical maintenance data, which achieves higher accuracy in failure prediction and early failure‐risk detection. After detecting hypervisors at risk, we perform live migration of virtual servers within a cluster, which decreases the load and prevents failures in the cloud. We performed a simulation for verification. According to the experimental results, our proposed model shows good accuracy in failure prediction and the possibility of decreasing downtime in a hypervisor service. Copyright © 2017 John Wiley & Sons, Ltd.

It is of great importance for service providers to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, the cumulative distribution function (CDF) of the inter-cell interference factor is derived. On this basis, closed-form expression of the maximum achievable rate subject to the interference from finite planar cells is analyzed over Nakagami-m fading uplink channels. Numerical results show that besides Nakagami-m fading severity parameters and path loss coefficient, the maximum achievable rate is also affected by the number of interference cells. According to the numerical results, with an increase in the number of interference cells, the maximum achievable rate is degraded.

Cooperative communication technologies can improve the system throughput energy efficiency and reliability in dynamic wireless networks. For practical multi-cell multi-antenna mobile cellular networks, co-channel interference is a critical issue affecting cooperative transmission (Co-Tx) performance. In this paper, we first derive a cooperative outage probability model and a cooperative block error rate (BLER) model incorporating a binary differential phase shift keying modulation for performance analysis in such cooperative cellular networks. Based on them, a cooperative energy efficiency model is proposed and analyzed under different Co-Tx scenarios, interference levels and wireless channel conditions. As demonstrated by numerical results, our analytical models show that Co-Tx is an effective approach to mitigate co-channel interference and improve the energy efficiency, BLER and overall outage probability performance in multi-cell multi-antenna cooperative cellular networks.

ABSTRACT Because of the resource constraints on sensor networks, many recent key management schemes exploit the location of nodes in order to establish pairwise keys. Despite these efforts, location discovery for sensor networks is very difficult because existing schemes have failed to consider changes in the signal range and the deployment error. As a result, network performance may accidentally decrease, whereas costs increase. In this paper, we used the theory of the signal range and deployment error knowledge to analyze sensor nodes location information. Combining with the probabilistic pre‐distribution method, we proposed a scheme that provides high connectivity and secure communication with better communication overhead by defining an efficient number of adjacent cells and determining the adequate length of the cell. With such optimistic results, novel deployment knowledge is also expected to provide superior performance with different types of key distribution schemes. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a primary path switching scheme to provide a seamless handover for dual-homed mobile terminals. This scheme is suggested as an enhancement to the mSCTP protocol. With our scheme, a mobile terminal performs primary path switching before it becomes unavailable due to its primary path drop. The improvement of the scheme comes from considering the temporal velocity of the mobile terminal with relative RTT variances of all available paths when it performs a handover process in the overlapped area between two different networks. Our simulation results show the proposed scheme provides a better overall performance than other schemes and the anticipatory switching is more important for faster moving terminals.

In this article, a video multicast protocol for multi-homed mobile terminals is proposed as an alternative stream control transmission protocol (SCTP) for partially reliable multicast services. It works with overlay peer-to-peer video multicast facility in the application layer. For a multi-homed mobile terminal, an error burst may occur when a handover is in process in the primary path switching procedure. The key issue concerned in this protocol is the ability to predict packet loss and to retransmit the lost packets as soon as a mobile terminal completes its primary path switching procedure. This property controls the delay sensitivity of transmissions. Conversely, the protocol can tolerate partial loss in video transmission as long as the loss is limited to a relatively short error burst. In addition, it reduces the message overhead significantly and provides a scalable communication mechanism for multicast applications. The performance improvement of the proposed protocol comes from 1) the estimation of temporal velocity of mobile terminals with lost packet prediction in a long error burst, and 2) the requirement for each mobile terminal to indicate which packets can be safely discarded from its agent.

Abstract In recent years, Wireless Sensor Networks (WSNs) have demonstrated successful applications for both civil and military tasks. However, sensor networks are susceptible to multiple types of attacks because they are randomly deployed in open and unprotected environments. It is necessary to utilize effective mechanisms to protect sensor networks against multiple types of attacks on routing protocols. In this paper, we propose a lightweight intrusion detection framework integrated for clustered sensor networks. Furthermore, we provide algorithms to minimize the triggered intrusion modules in clustered WSNs by using an over‐hearing mechanism to reduce the sending alert packets. Our scheme can prevent most routing attacks on sensor networks. In in‐depth simulation, the proposed scheme shows less energy consumption in intrusion detection than other schemes. Copyright © 2009 John Wiley & Sons, Ltd.

RFID Tag detection/recognition is one of the most critical issues for successful deployment of RFID systems in diverse applications. The main factors influencing tag detection by RFID reader antenna include tag position, relative position of reader, read field length, etc. In this paper, we analyze the characteristics of tag detection for a carton box object on a wooden pallet by an experimental approach based on tag signal strength, and we propose a method for predicting detection related directly to the strength of tag signal using an intelligent machine learning technique called support vector machine (SVM). The use of the proposed method is able to save time and cost by quick prediction of tag detection. Extensive experiments showed that the proposed approach can predict tag recognition for a carton box object with an accuracy at 95% for various reader heights and read field lengths. The proposed approach is effective for determining the best tag detection influencing factor conditioned on the target object with the help of detectability prediction.

The next generation network (NGN) must provide mobility and multiple interfaces to enable the seamless delivery of packets, in IP mobility environments, with the best possible quality of service (QoS) guarantee. This paper proposes an efficient resource reservation and QoS provisioning mechanism based on mobile SCTP (mSCTP) with network mobility and multi-homing for NGN. To do this, the proposed mechanism does not refresh the reserved resources on the common path through the path management at the time of mSCTP-based handover. The path management is accomplished by maintaining the common path and common routers on the path. To show the validity of the proposed mechanisms, the ns-2 simulator was used. it can provide the end-to-end seamless QoS and resources management in IP mobility environments more efficiently.

This paper proposes an energy efficient clustering algorithm for self-organizing and self-managing WSNs, called SNOW cluster. It introduces region node selection as well as cluster head election based on the residual battery capacity of nodes to reduce the costs of managing sensor nodes and of the communication among them. SNOW cluster clusters periodically to select cluster heads that are richer in residual energy level, compared to the other nodes to achieve load balancing and extend the lifetime of every individual sensor nodes. After the cluster heads are selected, SNOW cluster selects the region nodes in the cluster heads based on the energy level of them and chooses gateway nodes between clusters based on their location information. The effectiveness of the proposed SNOW cluster algorithm is validated through actual implementation and simulation using ns-2. The simulation results reveal that the proposed framework allows smaller energy consumption for network management and longer network lifetime than the existing schemes such as LEACH and LEACH-C for practical size networks.