Medium access control (MAC) is crucial for resource allocation in wireless body area networks (WBANs). However, existing MAC protocols often suffer from transmission conflicts and inefficient channel utilization. To address these issues, this paper proposes a Node-Differentiated Resource Scheduling (NDRS) MAC protocol, which dynamically allocates access resources based on node-specific requirements. This protocol employs a superframe structure consisting of a contention-based phase and a contention-free phase for data transmission. A Mamdani fuzzy inference system is utilized to calculate continuous node priorities. These priorities achieve fine-grained differentiation of node importance and thus serve as the foundation for transmission conflict minimization. During the contention-based phase, continuous and differentiated backoff times are assigned to nodes based on their priorities. These backoff times effectively reduce transmission collisions and enhance channel utilization. In the contention-free phase, time slots are preferentially allocated to nodes with higher priority, better channel utilization, and greater transmission reliability. This allocation thereby enhances channel usage efficiency and reduce transmission delays. This protocol is characterized by three key features: precise node prioritization, low transmission collisions, and high channel utilization. Extensive experimental results demonstrate that NDRS outperforms existing protocols in terms of average delay, throughput, packet loss ratio, and average energy consumption.

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments.

Machine learning (ML) has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying ML algorithms on resource-constrained edge devices poses significant challenges due to their limited computational capabilities and storage capacities. In this study, we propose leveraging tiny ML (TinyML) techniques to address these challenges and optimize channel allocation within long range wide area network (LoRaWAN) deployments. Our key innovation lies in replacing traditional random channel allocation methods with TinyML-based approaches, wherein each edge device autonomously utilizes TinyML to select the most efficient channel prior to each uplink transmission. Furthermore, we conduct comprehensive comparisons between TinyML and conventional channel allocation techniques implemented on edge devices. Through extensive simulations, our results demonstrate that TinyML outperforms existing channel allocation mechanisms in terms of packet success ratio (PSR). Notably, when evaluating TinyML against conventional ML approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data trans-mission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTnk protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

The rapid development of the Internet of Things (IoT) has led to the emergence of more and more novel applications in recent years. One of them is the e-health system, which can provide people with high-quality and convenient health care. Meanwhile, it is a key issue and challenge to protect the privacy and security of the user's personal health record. Some cryptographic methods have been proposed such as encrypt user's data before sharing it. However, it is complicated to share the data with multiple parties (doctors, health departments, etc.), due to the fact that data should be encrypted under each recipient's keys. Although several (t, n) threshold secret sharing schemes can share the data only need one encryption operation, there is a limitation that the decryption private key has to be reconstructed by one party. To offset this shortcoming, in this paper, we propose an efficient identity-based distributed decryption scheme for personal health record sharing system. It is convenient to share their data with multiple parties and does not require to reconstruct the decryption private key. We prove that our scheme is secure under chosen-ciphertext attack (CCA). Moreover, we implement our scheme by using the Java pairing-based cryptography (JPBC) library on a laptop and an Android phone. The experimental results show that our system is practical and effective in the electronic personal health record system.

Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN). However, relatively low charging utility exists in existing solutions. In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs. Charging MCs and server MCs are employed to jointly achieve our goal by three aspects. First, a path merging scheme is designed to save the traveling paths of MCs. Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas. Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs. Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs. Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs. The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles. Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility.

Content Centric Networks (CCN) has been evolved as a promising internet architecture that focuses on content centric approach for content requests rather than host centric approach. CCN provide in-network caching and content distribution capability improves Quality-of-Service by reducing intermediatory hop count and server load, which condequently reduces bandwidth requirements. Existing work in CCN emphasis on minimizing content caching operations and maximizing network hit ratio. In this paper, we have investigated the effect of in-network caching based on content provider distance and node centrality parameters over network hit ratio. A novel content placement approach named CPNDD (Content Placement based on Normalized Node Degree and Distance), has been proposed that collectively implement both parameters to intelligently select caching location in the network to maximize gain in hit ratio. The weightage of both parameters has been computed using extensive simulation on abilene network topology. We have compared our scheme with several peer caching algorithms in CCN. Simulation results are obtained for different cache size, exponent value of zipf distribution and number of requests. The results demonstrate that CPNDD increases in-network hit ratio gain upto 40% as comparison to existing algorithms.

The pervasive and smart healthcare is important for elderly patients which has revolutionized the medical world and caught the attention from industry and academia with the help of portable sensor-enabled devices. Tiny size and resource-constrained nature restricts them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the key challenges to be tackled carefully for ubiquitous healthcare. Energy efficiency, reliability and longer battery cycle are the vital ingredients for wearable devices to empower cost-effective and pervasive medical environment. Thus, this research work has three key contributions. First, a novel transmission power control driven energy efficient algorithm (EEA) is proposed to enhance energy, battery lifetime and reliability while monitoring the health status of elderly patients. Proposed EEA and conventional constant transmission power control (TPC) are evaluated by adopting real-time datasets of static (i.e., wheelchair sitting) and dynamic (i.e., wheelchair moving) body postures of elderly patients. Second, smart healthcare framework is proposed. Third, performance metrics such as, energy drain, battery lifetime and reliability are introduced and calculated by considering average and threshold RSSI and TPC values. Finally, it is observed through experimental analysis that the proposed EEA enhances energy efficiency with acceptable PLR than the constant TPC during data transmission.

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

Named data networking(NDN) is a very promising architecture for future network, which can improve the network performance due to its in-network caching feature. However, the pervasive caching is vulnerable against False-Locality Attack (FLA), one kind of cache pollution attack, where attackers repeatedly request a specific set of non-popular contents to replace popular contents. Therefore, the cache hit of legal requests is reduced and the response delay is increased. To mitigate this attack and improve the network performance, we propose a detection scheme based on Convolutional Neural Network (CNN) by fully exploiting the regularity of past requests. The input data of CNN are related to the inherent characteristics of the cached contents including the request ratio, the standard deviation of repeated Interests, the variance of request interval and the change of cache hit ratio. The output of CNN indicates whether FLA has been launched. Simulations through multi-topologies are conducted to validate the performance of our scheme. Compared with other state-of-the-art schemes, it is more effective in detecting FLA with higher detecting ratio, higher cache hit and lower hop count.

Missing values are a common phenomenon in real-world datasets, which decreases the quality and reliability of data mining. Traditional regression-based imputation method estimates missing values through the relationship between attributes inferred by complete records. In order to describe the relationship more appropriately and make better use of present values, a rule-based incomplete data modeling method is proposed to impute missing values in this paper. The method utilizes incomplete records together with complete records for establishing Takagi-Sugeno (TS) models. In this process, the incomplete dataset is divided into several subsets and the linear functions containing only significant variables are built to describe the relationships between attributes in each subset. Experimental results demonstrate that the proposed method can effectively improve the performance of missing value imputation.

In recent years, Industrial Wireless Sensor Network (IWSN) is gaining more popularity due to many applications in industries like fire detection, hazardous gas leakage detection, temperature monitoring, localization of sensors, etc. However, faulty sensors in the network may degrade the performance of the applications. In this paper, a software defined network (SDN) based fault detection method is proposed for IWSN. In this method, SDN plays an important role for controlling the whole system by setting a fault detection algorithm at the cluster heads (CHs). The CH periodically receives the monitoring data from the sensors and follows the fault detection algorithm set by the SDN to detect the faulty sensors in the network. The fault detection algorithm uses a statistical trimean method to detect the faulty sensors. Simulation results show that our proposed method performs better than Ji's fault detection method in terms of detection accuracy (DA) and false alarm rate (FAR). A IWSN prototype is also designed to evaluate the performance of the proposed method.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

Abstract In order to separate a host's identity from its location on the Internet, the Host Identity Protocol (HIP) was developed by the Internet Engineering Task Force as a mobility management solution. HIP provides a solid basis to enable secured mobility and multihoming features. Several extensions and proposals have been introduced in recent publications to improve the micro‐mobility features of HIP. Moreover, many other publications have dealt with the efficiency of Network Mobility (NEMO) management with HIP. However, the HIP‐based micro‐mobility management solutions adapted to NEMO scenario do not cover all security aspects requirements and still suffer from security flaws. Therefore, in this paper, a number of potential threats in the typical HIP with Rendez Vous Server are identified. A new secure and efficient scheme for network mobility management is also proposed to overcome the outlined ones. The proposed solution ensures strong authentication between network entities, reduces Denial of Service attacks, secures against Domain Name Server spoofing, reply, and eavesdropping attacks, and ensures end‐to‐end confidentiality and integrity protection. To analyze the security properties of the proposed scheme, we have performed automated formal specification and evaluation with the help of both the Automated Validation of Internet Security Protocols and Applications and the Security Protocol Animator, which have proved that authentication and confidentiality goals are achieved. Hence, the scheme is effective when an intruder is present. Copyright © 2014 John Wiley & Sons, Ltd.